oil shale: structure and reactionstom/oilshale/thf...shale gas vs oil shale • shale gas – from...

106
Oil Shale: Structure and Reactions 1 BYU Chemistry Department January 13, 2015 Thomas H. Fletcher James Hillier, Ryan Gillis, Jacob Adams, Dan Barfuss Chemical Engineering Dept., Brigham Young University Charles L. Mayne, Mark S. Solum, Ronald J. Pugmire Chemistry and Chemical Engineering Depts., University of Utah

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Page 1: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Oil Shale Structure and Reactions

1

BYU Chemistry DepartmentJanuary 13 2015

Thomas H FletcherJames Hillier Ryan Gillis Jacob Adams Dan Barfuss

Chemical Engineering Dept Brigham Young University

Charles L Mayne Mark S Solum Ronald J PugmireChemistry and Chemical Engineering Depts University of Utah

My Family

bull Tom Fletcherndash BS ChE BYU 1979ndash MS ChE BYU 1980ndash PhD ChE BYU 1983ndash Sandia National Labs Livermore Ca 1984-1991ndash Joined BYU ChE Dept in 1991

bull Harvey J Fletcher ndash Math Dept 1953-1992bull Harvey Fletcher ndash Physics College of

Engineering (1952-~1974)

2

My Research Low Grade Fuels

bull Coal pyrolysis combustion and gasificationndash High heating rates temperatures and pressures

bull Other fuelsndash Biomass petroleum coke heavy fuel oil oil shale

soot

bull Synfuel use in gas turbines (IGCC)bull Live fuels

ndash Wildland fires in shrub systems

3

Shale Gas vs Oil Shale

bull Shale gasndash From frackingndash Largely natural gasndash Some oil comes up with gas

bull Oil Shalendash Solid kerogen in shalendash Must be heated to permit flowndash Shale has very low porosity

4

5

World Oil Shale Reserves

6

Utah Colorado Wyoming

7

Proven World Petroleum Reserves(165 trillion barrels)

8

0 50 100 150 200 250 300 350

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

NigeriaLibya

RussiaUnited Arab Emirates

KuwaitIraqIran

CanadaSaudi Arabia

Venezuela

Proven Reserves (Billion Barrels)

Source US Energy Information Agency Data for 2014

Petroleum vs Oil Shale Reserves

9

0 500 1000 1500 2000 2500 3000 3500 4000 4500

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

Green River Oil ShaleNigeria

LibyaRussia

United Arab EmiratesKuwait

IraqIran

CanadaSaudi Arabia

Venezuela

Reserves (billion Barrels)

Industrial Approaches

bull In Situndash Leave rock in placendash Heat underground

bull Above Groundndash Retortndash Lined pit

10

AMSO Process (Total)

Eco-Shale Process (RedLeaf)

bull I nicknamed this the ldquoluaurdquo

Alberta Taciuk Processor retort

(similar to Enefit process)

So Why Arenrsquot We Producing Oil from Oil Shale

bull Price (Estimates range from $35 to 70bbl)bull Environmental Concerns

ndash Sage Grousendash Water amp Air Qualityndash Land usepermitting

bull Pre-refining of oilndash High nitrogenndash Wax content

15

Objective

bull Determine the changes in macromolecular structure of oil shale kerogen during pyrolysisndash Three samples taken from a core in the Uintah

Basin (Utah)bull GR1 GR2 GR3 (parent oil shale)bull GR19 GR29 GR39 (demineralized kerogen)

ndash Focus on modern 13C NMR characterizationbull GCMSbull FTIRbull TGA

16

Skyline 16 Core

GR1GR2

GR3

1000 foot core drilled Spring 2010Green River Formation Uintah Basin Utah

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 2: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

My Family

bull Tom Fletcherndash BS ChE BYU 1979ndash MS ChE BYU 1980ndash PhD ChE BYU 1983ndash Sandia National Labs Livermore Ca 1984-1991ndash Joined BYU ChE Dept in 1991

bull Harvey J Fletcher ndash Math Dept 1953-1992bull Harvey Fletcher ndash Physics College of

Engineering (1952-~1974)

2

My Research Low Grade Fuels

bull Coal pyrolysis combustion and gasificationndash High heating rates temperatures and pressures

bull Other fuelsndash Biomass petroleum coke heavy fuel oil oil shale

soot

bull Synfuel use in gas turbines (IGCC)bull Live fuels

ndash Wildland fires in shrub systems

3

Shale Gas vs Oil Shale

bull Shale gasndash From frackingndash Largely natural gasndash Some oil comes up with gas

bull Oil Shalendash Solid kerogen in shalendash Must be heated to permit flowndash Shale has very low porosity

4

5

World Oil Shale Reserves

6

Utah Colorado Wyoming

7

Proven World Petroleum Reserves(165 trillion barrels)

8

0 50 100 150 200 250 300 350

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

NigeriaLibya

RussiaUnited Arab Emirates

KuwaitIraqIran

CanadaSaudi Arabia

Venezuela

Proven Reserves (Billion Barrels)

Source US Energy Information Agency Data for 2014

Petroleum vs Oil Shale Reserves

9

0 500 1000 1500 2000 2500 3000 3500 4000 4500

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

Green River Oil ShaleNigeria

LibyaRussia

United Arab EmiratesKuwait

IraqIran

CanadaSaudi Arabia

Venezuela

Reserves (billion Barrels)

Industrial Approaches

bull In Situndash Leave rock in placendash Heat underground

bull Above Groundndash Retortndash Lined pit

10

AMSO Process (Total)

Eco-Shale Process (RedLeaf)

bull I nicknamed this the ldquoluaurdquo

Alberta Taciuk Processor retort

(similar to Enefit process)

So Why Arenrsquot We Producing Oil from Oil Shale

bull Price (Estimates range from $35 to 70bbl)bull Environmental Concerns

ndash Sage Grousendash Water amp Air Qualityndash Land usepermitting

bull Pre-refining of oilndash High nitrogenndash Wax content

15

Objective

bull Determine the changes in macromolecular structure of oil shale kerogen during pyrolysisndash Three samples taken from a core in the Uintah

Basin (Utah)bull GR1 GR2 GR3 (parent oil shale)bull GR19 GR29 GR39 (demineralized kerogen)

ndash Focus on modern 13C NMR characterizationbull GCMSbull FTIRbull TGA

16

Skyline 16 Core

GR1GR2

GR3

1000 foot core drilled Spring 2010Green River Formation Uintah Basin Utah

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 3: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

My Research Low Grade Fuels

bull Coal pyrolysis combustion and gasificationndash High heating rates temperatures and pressures

bull Other fuelsndash Biomass petroleum coke heavy fuel oil oil shale

soot

bull Synfuel use in gas turbines (IGCC)bull Live fuels

ndash Wildland fires in shrub systems

3

Shale Gas vs Oil Shale

bull Shale gasndash From frackingndash Largely natural gasndash Some oil comes up with gas

bull Oil Shalendash Solid kerogen in shalendash Must be heated to permit flowndash Shale has very low porosity

4

5

World Oil Shale Reserves

6

Utah Colorado Wyoming

7

Proven World Petroleum Reserves(165 trillion barrels)

8

0 50 100 150 200 250 300 350

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

NigeriaLibya

RussiaUnited Arab Emirates

KuwaitIraqIran

CanadaSaudi Arabia

Venezuela

Proven Reserves (Billion Barrels)

Source US Energy Information Agency Data for 2014

Petroleum vs Oil Shale Reserves

9

0 500 1000 1500 2000 2500 3000 3500 4000 4500

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

Green River Oil ShaleNigeria

LibyaRussia

United Arab EmiratesKuwait

IraqIran

CanadaSaudi Arabia

Venezuela

Reserves (billion Barrels)

Industrial Approaches

bull In Situndash Leave rock in placendash Heat underground

bull Above Groundndash Retortndash Lined pit

10

AMSO Process (Total)

Eco-Shale Process (RedLeaf)

bull I nicknamed this the ldquoluaurdquo

Alberta Taciuk Processor retort

(similar to Enefit process)

So Why Arenrsquot We Producing Oil from Oil Shale

bull Price (Estimates range from $35 to 70bbl)bull Environmental Concerns

ndash Sage Grousendash Water amp Air Qualityndash Land usepermitting

bull Pre-refining of oilndash High nitrogenndash Wax content

15

Objective

bull Determine the changes in macromolecular structure of oil shale kerogen during pyrolysisndash Three samples taken from a core in the Uintah

Basin (Utah)bull GR1 GR2 GR3 (parent oil shale)bull GR19 GR29 GR39 (demineralized kerogen)

ndash Focus on modern 13C NMR characterizationbull GCMSbull FTIRbull TGA

16

Skyline 16 Core

GR1GR2

GR3

1000 foot core drilled Spring 2010Green River Formation Uintah Basin Utah

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 4: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Shale Gas vs Oil Shale

bull Shale gasndash From frackingndash Largely natural gasndash Some oil comes up with gas

bull Oil Shalendash Solid kerogen in shalendash Must be heated to permit flowndash Shale has very low porosity

4

5

World Oil Shale Reserves

6

Utah Colorado Wyoming

7

Proven World Petroleum Reserves(165 trillion barrels)

8

0 50 100 150 200 250 300 350

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

NigeriaLibya

RussiaUnited Arab Emirates

KuwaitIraqIran

CanadaSaudi Arabia

Venezuela

Proven Reserves (Billion Barrels)

Source US Energy Information Agency Data for 2014

Petroleum vs Oil Shale Reserves

9

0 500 1000 1500 2000 2500 3000 3500 4000 4500

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

Green River Oil ShaleNigeria

LibyaRussia

United Arab EmiratesKuwait

IraqIran

CanadaSaudi Arabia

Venezuela

Reserves (billion Barrels)

Industrial Approaches

bull In Situndash Leave rock in placendash Heat underground

bull Above Groundndash Retortndash Lined pit

10

AMSO Process (Total)

Eco-Shale Process (RedLeaf)

bull I nicknamed this the ldquoluaurdquo

Alberta Taciuk Processor retort

(similar to Enefit process)

So Why Arenrsquot We Producing Oil from Oil Shale

bull Price (Estimates range from $35 to 70bbl)bull Environmental Concerns

ndash Sage Grousendash Water amp Air Qualityndash Land usepermitting

bull Pre-refining of oilndash High nitrogenndash Wax content

15

Objective

bull Determine the changes in macromolecular structure of oil shale kerogen during pyrolysisndash Three samples taken from a core in the Uintah

Basin (Utah)bull GR1 GR2 GR3 (parent oil shale)bull GR19 GR29 GR39 (demineralized kerogen)

ndash Focus on modern 13C NMR characterizationbull GCMSbull FTIRbull TGA

16

Skyline 16 Core

GR1GR2

GR3

1000 foot core drilled Spring 2010Green River Formation Uintah Basin Utah

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 5: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

5

World Oil Shale Reserves

6

Utah Colorado Wyoming

7

Proven World Petroleum Reserves(165 trillion barrels)

8

0 50 100 150 200 250 300 350

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

NigeriaLibya

RussiaUnited Arab Emirates

KuwaitIraqIran

CanadaSaudi Arabia

Venezuela

Proven Reserves (Billion Barrels)

Source US Energy Information Agency Data for 2014

Petroleum vs Oil Shale Reserves

9

0 500 1000 1500 2000 2500 3000 3500 4000 4500

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

Green River Oil ShaleNigeria

LibyaRussia

United Arab EmiratesKuwait

IraqIran

CanadaSaudi Arabia

Venezuela

Reserves (billion Barrels)

Industrial Approaches

bull In Situndash Leave rock in placendash Heat underground

bull Above Groundndash Retortndash Lined pit

10

AMSO Process (Total)

Eco-Shale Process (RedLeaf)

bull I nicknamed this the ldquoluaurdquo

Alberta Taciuk Processor retort

(similar to Enefit process)

So Why Arenrsquot We Producing Oil from Oil Shale

bull Price (Estimates range from $35 to 70bbl)bull Environmental Concerns

ndash Sage Grousendash Water amp Air Qualityndash Land usepermitting

bull Pre-refining of oilndash High nitrogenndash Wax content

15

Objective

bull Determine the changes in macromolecular structure of oil shale kerogen during pyrolysisndash Three samples taken from a core in the Uintah

Basin (Utah)bull GR1 GR2 GR3 (parent oil shale)bull GR19 GR29 GR39 (demineralized kerogen)

ndash Focus on modern 13C NMR characterizationbull GCMSbull FTIRbull TGA

16

Skyline 16 Core

GR1GR2

GR3

1000 foot core drilled Spring 2010Green River Formation Uintah Basin Utah

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 6: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

World Oil Shale Reserves

6

Utah Colorado Wyoming

7

Proven World Petroleum Reserves(165 trillion barrels)

8

0 50 100 150 200 250 300 350

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

NigeriaLibya

RussiaUnited Arab Emirates

KuwaitIraqIran

CanadaSaudi Arabia

Venezuela

Proven Reserves (Billion Barrels)

Source US Energy Information Agency Data for 2014

Petroleum vs Oil Shale Reserves

9

0 500 1000 1500 2000 2500 3000 3500 4000 4500

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

Green River Oil ShaleNigeria

LibyaRussia

United Arab EmiratesKuwait

IraqIran

CanadaSaudi Arabia

Venezuela

Reserves (billion Barrels)

Industrial Approaches

bull In Situndash Leave rock in placendash Heat underground

bull Above Groundndash Retortndash Lined pit

10

AMSO Process (Total)

Eco-Shale Process (RedLeaf)

bull I nicknamed this the ldquoluaurdquo

Alberta Taciuk Processor retort

(similar to Enefit process)

So Why Arenrsquot We Producing Oil from Oil Shale

bull Price (Estimates range from $35 to 70bbl)bull Environmental Concerns

ndash Sage Grousendash Water amp Air Qualityndash Land usepermitting

bull Pre-refining of oilndash High nitrogenndash Wax content

15

Objective

bull Determine the changes in macromolecular structure of oil shale kerogen during pyrolysisndash Three samples taken from a core in the Uintah

Basin (Utah)bull GR1 GR2 GR3 (parent oil shale)bull GR19 GR29 GR39 (demineralized kerogen)

ndash Focus on modern 13C NMR characterizationbull GCMSbull FTIRbull TGA

16

Skyline 16 Core

GR1GR2

GR3

1000 foot core drilled Spring 2010Green River Formation Uintah Basin Utah

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 7: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Utah Colorado Wyoming

7

Proven World Petroleum Reserves(165 trillion barrels)

8

0 50 100 150 200 250 300 350

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

NigeriaLibya

RussiaUnited Arab Emirates

KuwaitIraqIran

CanadaSaudi Arabia

Venezuela

Proven Reserves (Billion Barrels)

Source US Energy Information Agency Data for 2014

Petroleum vs Oil Shale Reserves

9

0 500 1000 1500 2000 2500 3000 3500 4000 4500

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

Green River Oil ShaleNigeria

LibyaRussia

United Arab EmiratesKuwait

IraqIran

CanadaSaudi Arabia

Venezuela

Reserves (billion Barrels)

Industrial Approaches

bull In Situndash Leave rock in placendash Heat underground

bull Above Groundndash Retortndash Lined pit

10

AMSO Process (Total)

Eco-Shale Process (RedLeaf)

bull I nicknamed this the ldquoluaurdquo

Alberta Taciuk Processor retort

(similar to Enefit process)

So Why Arenrsquot We Producing Oil from Oil Shale

bull Price (Estimates range from $35 to 70bbl)bull Environmental Concerns

ndash Sage Grousendash Water amp Air Qualityndash Land usepermitting

bull Pre-refining of oilndash High nitrogenndash Wax content

15

Objective

bull Determine the changes in macromolecular structure of oil shale kerogen during pyrolysisndash Three samples taken from a core in the Uintah

Basin (Utah)bull GR1 GR2 GR3 (parent oil shale)bull GR19 GR29 GR39 (demineralized kerogen)

ndash Focus on modern 13C NMR characterizationbull GCMSbull FTIRbull TGA

16

Skyline 16 Core

GR1GR2

GR3

1000 foot core drilled Spring 2010Green River Formation Uintah Basin Utah

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 8: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Proven World Petroleum Reserves(165 trillion barrels)

8

0 50 100 150 200 250 300 350

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

NigeriaLibya

RussiaUnited Arab Emirates

KuwaitIraqIran

CanadaSaudi Arabia

Venezuela

Proven Reserves (Billion Barrels)

Source US Energy Information Agency Data for 2014

Petroleum vs Oil Shale Reserves

9

0 500 1000 1500 2000 2500 3000 3500 4000 4500

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

Green River Oil ShaleNigeria

LibyaRussia

United Arab EmiratesKuwait

IraqIran

CanadaSaudi Arabia

Venezuela

Reserves (billion Barrels)

Industrial Approaches

bull In Situndash Leave rock in placendash Heat underground

bull Above Groundndash Retortndash Lined pit

10

AMSO Process (Total)

Eco-Shale Process (RedLeaf)

bull I nicknamed this the ldquoluaurdquo

Alberta Taciuk Processor retort

(similar to Enefit process)

So Why Arenrsquot We Producing Oil from Oil Shale

bull Price (Estimates range from $35 to 70bbl)bull Environmental Concerns

ndash Sage Grousendash Water amp Air Qualityndash Land usepermitting

bull Pre-refining of oilndash High nitrogenndash Wax content

15

Objective

bull Determine the changes in macromolecular structure of oil shale kerogen during pyrolysisndash Three samples taken from a core in the Uintah

Basin (Utah)bull GR1 GR2 GR3 (parent oil shale)bull GR19 GR29 GR39 (demineralized kerogen)

ndash Focus on modern 13C NMR characterizationbull GCMSbull FTIRbull TGA

16

Skyline 16 Core

GR1GR2

GR3

1000 foot core drilled Spring 2010Green River Formation Uintah Basin Utah

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 9: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Petroleum vs Oil Shale Reserves

9

0 500 1000 1500 2000 2500 3000 3500 4000 4500

EcuadorMexicoAngolaAlgeria

BrazilChinaQatar

KazakhstanUnited States

Green River Oil ShaleNigeria

LibyaRussia

United Arab EmiratesKuwait

IraqIran

CanadaSaudi Arabia

Venezuela

Reserves (billion Barrels)

Industrial Approaches

bull In Situndash Leave rock in placendash Heat underground

bull Above Groundndash Retortndash Lined pit

10

AMSO Process (Total)

Eco-Shale Process (RedLeaf)

bull I nicknamed this the ldquoluaurdquo

Alberta Taciuk Processor retort

(similar to Enefit process)

So Why Arenrsquot We Producing Oil from Oil Shale

bull Price (Estimates range from $35 to 70bbl)bull Environmental Concerns

ndash Sage Grousendash Water amp Air Qualityndash Land usepermitting

bull Pre-refining of oilndash High nitrogenndash Wax content

15

Objective

bull Determine the changes in macromolecular structure of oil shale kerogen during pyrolysisndash Three samples taken from a core in the Uintah

Basin (Utah)bull GR1 GR2 GR3 (parent oil shale)bull GR19 GR29 GR39 (demineralized kerogen)

ndash Focus on modern 13C NMR characterizationbull GCMSbull FTIRbull TGA

16

Skyline 16 Core

GR1GR2

GR3

1000 foot core drilled Spring 2010Green River Formation Uintah Basin Utah

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 10: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Industrial Approaches

bull In Situndash Leave rock in placendash Heat underground

bull Above Groundndash Retortndash Lined pit

10

AMSO Process (Total)

Eco-Shale Process (RedLeaf)

bull I nicknamed this the ldquoluaurdquo

Alberta Taciuk Processor retort

(similar to Enefit process)

So Why Arenrsquot We Producing Oil from Oil Shale

bull Price (Estimates range from $35 to 70bbl)bull Environmental Concerns

ndash Sage Grousendash Water amp Air Qualityndash Land usepermitting

bull Pre-refining of oilndash High nitrogenndash Wax content

15

Objective

bull Determine the changes in macromolecular structure of oil shale kerogen during pyrolysisndash Three samples taken from a core in the Uintah

Basin (Utah)bull GR1 GR2 GR3 (parent oil shale)bull GR19 GR29 GR39 (demineralized kerogen)

ndash Focus on modern 13C NMR characterizationbull GCMSbull FTIRbull TGA

16

Skyline 16 Core

GR1GR2

GR3

1000 foot core drilled Spring 2010Green River Formation Uintah Basin Utah

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 11: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

AMSO Process (Total)

Eco-Shale Process (RedLeaf)

bull I nicknamed this the ldquoluaurdquo

Alberta Taciuk Processor retort

(similar to Enefit process)

So Why Arenrsquot We Producing Oil from Oil Shale

bull Price (Estimates range from $35 to 70bbl)bull Environmental Concerns

ndash Sage Grousendash Water amp Air Qualityndash Land usepermitting

bull Pre-refining of oilndash High nitrogenndash Wax content

15

Objective

bull Determine the changes in macromolecular structure of oil shale kerogen during pyrolysisndash Three samples taken from a core in the Uintah

Basin (Utah)bull GR1 GR2 GR3 (parent oil shale)bull GR19 GR29 GR39 (demineralized kerogen)

ndash Focus on modern 13C NMR characterizationbull GCMSbull FTIRbull TGA

16

Skyline 16 Core

GR1GR2

GR3

1000 foot core drilled Spring 2010Green River Formation Uintah Basin Utah

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 12: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Eco-Shale Process (RedLeaf)

bull I nicknamed this the ldquoluaurdquo

Alberta Taciuk Processor retort

(similar to Enefit process)

So Why Arenrsquot We Producing Oil from Oil Shale

bull Price (Estimates range from $35 to 70bbl)bull Environmental Concerns

ndash Sage Grousendash Water amp Air Qualityndash Land usepermitting

bull Pre-refining of oilndash High nitrogenndash Wax content

15

Objective

bull Determine the changes in macromolecular structure of oil shale kerogen during pyrolysisndash Three samples taken from a core in the Uintah

Basin (Utah)bull GR1 GR2 GR3 (parent oil shale)bull GR19 GR29 GR39 (demineralized kerogen)

ndash Focus on modern 13C NMR characterizationbull GCMSbull FTIRbull TGA

16

Skyline 16 Core

GR1GR2

GR3

1000 foot core drilled Spring 2010Green River Formation Uintah Basin Utah

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 13: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Alberta Taciuk Processor retort

(similar to Enefit process)

So Why Arenrsquot We Producing Oil from Oil Shale

bull Price (Estimates range from $35 to 70bbl)bull Environmental Concerns

ndash Sage Grousendash Water amp Air Qualityndash Land usepermitting

bull Pre-refining of oilndash High nitrogenndash Wax content

15

Objective

bull Determine the changes in macromolecular structure of oil shale kerogen during pyrolysisndash Three samples taken from a core in the Uintah

Basin (Utah)bull GR1 GR2 GR3 (parent oil shale)bull GR19 GR29 GR39 (demineralized kerogen)

ndash Focus on modern 13C NMR characterizationbull GCMSbull FTIRbull TGA

16

Skyline 16 Core

GR1GR2

GR3

1000 foot core drilled Spring 2010Green River Formation Uintah Basin Utah

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 14: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

So Why Arenrsquot We Producing Oil from Oil Shale

bull Price (Estimates range from $35 to 70bbl)bull Environmental Concerns

ndash Sage Grousendash Water amp Air Qualityndash Land usepermitting

bull Pre-refining of oilndash High nitrogenndash Wax content

15

Objective

bull Determine the changes in macromolecular structure of oil shale kerogen during pyrolysisndash Three samples taken from a core in the Uintah

Basin (Utah)bull GR1 GR2 GR3 (parent oil shale)bull GR19 GR29 GR39 (demineralized kerogen)

ndash Focus on modern 13C NMR characterizationbull GCMSbull FTIRbull TGA

16

Skyline 16 Core

GR1GR2

GR3

1000 foot core drilled Spring 2010Green River Formation Uintah Basin Utah

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 15: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Objective

bull Determine the changes in macromolecular structure of oil shale kerogen during pyrolysisndash Three samples taken from a core in the Uintah

Basin (Utah)bull GR1 GR2 GR3 (parent oil shale)bull GR19 GR29 GR39 (demineralized kerogen)

ndash Focus on modern 13C NMR characterizationbull GCMSbull FTIRbull TGA

16

Skyline 16 Core

GR1GR2

GR3

1000 foot core drilled Spring 2010Green River Formation Uintah Basin Utah

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 16: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Skyline 16 Core

GR1GR2

GR3

1000 foot core drilled Spring 2010Green River Formation Uintah Basin Utah

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 17: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Kerogen vs Bitumen inGreen River Oil Shale

Two types of hydrocarbons in oil shalebull Bitumen

ndash Extractable using organic solventsndash ~12 of hydrocarbon in GROS studied

bull Kerogenndash Not extractable using organic solventndash ~88 of hydrocarbons in GROS

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 18: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Siskinrsquos 1995 2Dmodel for a type Ikerogen from a Green River oilshale

Siskin et al in CompositionGeochemistry and Conversionof Oil Shales (NATO ASISeries C volume 455) 1995

2D Kerogen Model

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 19: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

9-Step Demineralization Process

After Vandegrift Winans Scott Horowitz Fuel 1980

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 20: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Table1 Moisture and Ash Analyses of the GR1 GR2

and GR3 Samples

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Wt of parent shale

GR1

GR2

GR3

Avg Standard Deviation

Moisture

0415

0265

038

0066

Ash

7364

8544

7911

0038

Organic

2595

1580

2051

071

Page 21: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Ultimate Analysis of GR1 GR2 and GR3 Samples by Huffman Laboratories

dry ash-free

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane

GR19

GR29

GR39

Moisture (Wt as recrsquod)

077

039

054

C (Wt daf)

7737

7753

7617

H

979

995

951

N

277

257

253

O (diff)

807

801

809

S

201

196

371

Ash (Wt dry)

530

460

387

Page 22: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Bitumen Analysisbull Bitumen extracted with CH3OHCH2Cl2bull Yield = 12 of hydrocarbon in oil shalebull Carbon-13 NMR results

- All 3 samples are quite similar- Dominated by long chain alkanes with average

length ~23 plusmn 3 carbons- 92 plusmn 2 aliphatic- Few CH carbons indicating few branch points- Essentially no non-protonated aliphatic carbons- Approximately 50-60 of aromatic carbons are

protonated

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 23: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

a

Aromatic carbons

Aliphatic carbonsCarbon-13 spectrumof GR3 bitumen

CH3 of alpha-alkanes

CH2 ofn-aliphaticchains

Quant C spectrum of GR2 bitumen dissolved in CD2Cl2 showing the aliphatic (9105) to aromatic carbon (895) ratio

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 24: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

- Goal Characterize the solid hydrocarbons (kerogen) without reaction

- Analytical Technique Solid-State C-13 NMR

- 14 Structural parameters and 8 lattice parameters were developed for coal bull Applied to oil shale kerogen and other

hydrocarbons

NMR Studies of Kerogen

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 25: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Empirical Relationship to get of CCluster

From Solum et alEnergy amp Fuels (1989)

Fraction ofBridgeheadCarbons

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 26: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Chemical Structure Relationships

σ+1 = coordination number(avg number of attachments per cluster)

Attachments = side chains + bridges + loops (not hydrogen)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 27: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 28: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Solid State NMR on Demineralized Kerogens(GR123)

bull Aromaticity (farsquo) = 0193bull Average Aromatic Cluster Size (C)

= 103 ~ (naphthalene)bull Average Aliphatic Chain Length = 115bull This means bridge length = 23bull Fraction of aromatics with attachments

= 05bull Average cluster molecular weight

= 832 daltonminus This includes aromatic cluster plus

side chains and half the bridgesbull Average attachment molecular weight

= 138 dalton

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 29: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Reactions

31

kerogen

Light gas (non-condensable gas)

Tar (condensable gas) ---- Oil

Char (solid residue) ---- Coke

Heat

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 30: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Kerogen Retort

bull Kerogen = Demineralized oil shalebull 1 g dispersed initially in reaction tubebull 10 Kmin heating ratebull Purged with 1 Lmin N2bull Effluent gases condensed on cool trap

bull Propanoldry icebull Tars removed by dichloromethane

bull Deuterated for proton NMRbull Char removed by tapping reaction tubebull Transfer bag collects light gases for FTIR

analysisbull Apparatus removed from heater to cool when

desired temperature reachedbull Tar and char yield by weighing apparatusbull Gas yield by difference

Wanted Tar and char in quantities sufficient for NMR and other analyses

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 31: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Kerogen Retort Data

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 32: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Kerogen Retort Data

bull Main tar release at 400 to 475degCbull Little change before 350degC bull Carbonate decomposition to CO2 at

~575 degCbull 60 tar yield for GR19 and GR29

ndash 69 tar yield for GR39

bull ~20 char yield for all three samplesndash ~80 pyrolyzed

bull Analysisndash Tar rarr 13C NMR (liquid)ndash Char rarr 13C NMR (solid)ndash Gas rarr FTIR

bull 3 temperature ranges

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 33: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Oil Shale vs Kerogen

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 34: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

36

AliphaticAromatic

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 35: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Structure Parameters of Char

37

Structural Parameter GR19 300degC 375degC 410degC 445degC 495degC

aromatic carbon fa = farsquo+faC 024 023 028 028 035 073

carbonyl faC 004 004 003 001 003 004

aldehydes and ketones faO 001 002 0015 0005 001 001

acids and esters faOO 003 002 0015 0005 002 003

aromatic carbon carbonyl subtracted 119891119891119886119886 primersquo 02 019 025 027 032 069 protonated aromatic carbon fa

H 006 005 008 009 012 011 nonprotonated aromatic carbon fa

N=faP+fa

S+faB 014 014 017 018 02 058

aromatic carbon with oxygen attachment (phenolic) fa

P 003 003 004 003 004 008 aromatic carbon with alkyl attachment fa

S 007 006 008 007 01 019 aromatic bridgehead and inner carbon fa

B 004 005 005 008 006 031 aliphatic carbon fal 076 077 072 072 065 027

aliphatic CH and CH2 falH 065 066 063 061 054 019

aliphatic CH3 and nonprotonated carbon fal

011 011 009 011 011 008 aliphatic with oxygen attachment fal

O 0 001 001 001 001 001 aromatic bridgehead carbons χb 02 0263 02 0296 0188 0449

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 36: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Lattice Parameters of Char

38

Lattice parameter GR19 300degC 375degC 410degC 445degC 495degC average number of carbons per cluster C 10 126 10 144 96 219 total attachments per cluster σ + 1 5 6 48 53 42 84 fraction of intact bridges per cluster p0 -01 -022 025 -001 021 07

bridges and loops per cluster BC -- -- 12 -- 09 59 side chains per cluster SC -- -- 36 -- 33 25

molecular weight per cluster MWcl 776 -- -- -- molecular weight per side chain mδ 131 -- -- -- Ratio falfa

S 1086 1283 900 1029 650 142 Mass Release (wt of parent kerogen) 00 71 71 172 410 776

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 37: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

One problem with NMR analysis

bull Fraction of intact bridges P0ndash Assumes that each chain is terminated with a

methyl groupndash Assumes no branching in side chain

bull Some P0 values in kerogen are negativendash One of the above assumptions is not valid

39

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 38: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Aromaticity of Char

40Open symbols are GR39 solid symbols are GR19 and GR29

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 39: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Changes in Lattice Structure of Char

41

Open symbols are GR39 solid symbols are GR19 and GR29

bull Note that side chains are one-half of a bridgebull Bridge length changes from 24 to 2

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 40: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)

42Hillier et al accepted for publication Ind Eng Chem Res (2013)

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 41: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Carbon-13 NMR of Tars

43

QuantitativeCarbon-13

QuantitativeCarbon-13(protonated only)

-CH=CH2-CH=CH2

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 42: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Carbon-13 NMR of Tars

bull All 3 core samples similarbull Strong signal from terminal alkenes

(α elimination)bull 19 aromatic plus alkenes

ndash 58 of aromatic carbons are protonated (H attached)

bull Aliphatic carbon dominated by n-alkyl chainsndash 20 carbons average chain lengthndash Essentially no non-protonated carbons

bull Not much change seen with pyrolysis temperature

44

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 43: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

GCMS Analysis of Tars

45

C7

C8 C9 C10

C11

C12

C13

C14

Pyre

neC1

5

C16

C17

prist

ane

C18

C19

C20

C21

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 44: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

GCMS Analysis of Tars

46

bull Alkanealkene pairsbull Up to C24

bull Pristane (branched)bull Pyrene (aromatic)

Selected Identified Peaks from the Gas ChromatogramTime (min) Compound Time (min) Compound

578 1-Heptene 217 Tetradecane596 Heptane 2284 2-Pentadecanone821 1-Octene 234 1-Pentadecene841 Octane 2353 Pentadecane107 1-Nonene 2461 Pyrene

1092 Nonane 2519 1-Hexadecene1315 1-Decene 253 Hexadecane1335 Decane 2612 2-Heptadecanone1547 1-Undecene 2693 1-Heptadecene1563 Undecane 2704 Heptadecane

176 1-Dodecene 2758 Pristane1777 Dodecane 2867 1-Octadecene1805 2-Tridecanol 2879 Octadecane1922 Dodecanone 3062 Nonadecane1965 1-Tridencene 3264 Eicosane1972 2-Tridecanone 3525 Heneicosane1979 Tridecane 3839 Docosane

213 2-Tetradecanone 4268 Tricosane2157 1-Tetradecene 484 Tetracosane

Sheet1

Sheet2

Sheet3

image1emf

403530252015105

Time on Column (min)

425degC

445degC

475degC

480degC

525degC

575degC

GR 29

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Column1 Column2 Column3 Column4
Selected Identified Peaks from the Gas Chromatogram
Time (min) Compound Time (min) Compound
578 1-Heptene 217 Tetradecane
596 Heptane 2284 2-Pentadecanone
821 1-Octene 234 1-Pentadecene
841 Octane 2353 Pentadecane
107 1-Nonene 2461 Pyrene
1092 Nonane 2519 1-Hexadecene
1315 1-Decene 253 Hexadecane
1335 Decane 2612 2-Heptadecanone
1547 1-Undecene 2693 1-Heptadecene
1563 Undecane 2704 Heptadecane
176 1-Dodecene 2758 Pristane
1777 Dodecane 2867 1-Octadecene
1805 2-Tridecanol 2879 Octadecane
1922 Dodecanone 3062 Nonadecane
1965 1-Tridencene 3264 Eicosane
1972 2-Tridecanone 3525 Heneicosane
1979 Tridecane 3839 Docosane
213 2-Tetradecanone 4268 Tricosane
2157 1-Tetradecene 484 Tetracosane
Page 45: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Alkane and Alkene Generation

47

+

+

α-elimination

β-elimination

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Page 46: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

FTIR Gas AnalysisLight GR19 gases collected for 3 temperature intervals

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Page 47: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

FTIR Gas Analysis

49

0102030405060708090

100

GR19 225-325(degC)

GR19 225-425(degC)

GR19 225-525(degC)

Wt

of E

volv

ed L

ight

Gas

GR19

Other

CH4

CO

CO2

0102030405060708090

100

GR39 225-425 (degC) GR39 225-525 (degC)W

t o

f Evo

lved

Lig

ht G

as

GR39

Other

CH4

CO

CO2

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Page 48: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Application to Chemical Percolation Devolatilization (CPD) Model

bull Start with measured lattice parametersndash P0 = 05 (assumed)

bull Kinetic rates from TGA data at 3 heating ratesndash 1 5 and 10 Kmin

bull No yield parameters specified

bull 85 of gas assumed to condensendash Becomes part of tar

50

bull Specification of chemical structure from NMR data

bull Bridge-breaking based on kinetic rate

bull Percolation-based lattice breakupbull Vapor-liquid equilibrium schemebull Crosslinking

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Page 49: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Summary and Conclusions

bull Demineralized kerogen from three oil shale samples were pyrolyzed at 10 Kmin in N2ndash ~80 volatiles achieved (60 to 69 tar)ndash Narrow temperature window for pyrolysis between 400

and 475degCndash Small difference in rates between oil shale and kerogen

bull 13C NMR on charndash Char aromaticity reached ~80ndash Aromatic Ccluster increased from 12 to over 20ndash Attachmentscluster increased from 5 to 8ndash Side chain length decreased from 12 carbons to 1

bull Bridge length changes from 24 carbons to 251

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Page 50: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Summary and Conclusions(continued)

bull 13C NMR on tarndash Tar aromaticity was ~19ndash 58 of aromatic carbons are protonated

(H attached)ndash Aliphatic carbon dominated by n-alkyl chains

bull 20 carbons average chain length

ndash Essentially no non-protonated carbonsndash Not much change seen with pyrolysis temperature

52

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Page 51: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Summary and Conclusions(continued)

bull GCMS of tarndash Alkane1-alkene pairs between 7 and 24 carbonsndash Largest peaks observed for chains with 15 to 17

carbons

bull FTIR of gasndash CH4 CO and CO2 increase from 40 to ~65 wt of

gas samples as temperature increasesbull CO2 is largest component (40 of light gas at 525degC)

53

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Page 52: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Acknowledgment

This material is based upon work supported by the Department of Energy under Award Number DE-NT0005015 The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof

54

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Page 53: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Thank You

55

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Page 54: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

13C NMR Analysis of Kerogen Chars

56

(Single Pulse) (Cross Polarization)

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Page 55: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis

57

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Page 56: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Thermo-Gravimetric Analysis

Experimental Matrixbull Parent oil shale

ndash GR1 GR2 GR3ndash Ground to ~100 microm

bull Why pressurendash In-situ recovery will be at

pressure caused by overburden

bull Multiple heating ratesndash Permits more accurate kinetic

coefficientsNeeded to replicate samples to get statistics on kinetics

GR1Atm X X X X X X40 Bar X X X X X X

GR2Atm X X X X X X40 Bar X X X X X X

GR3Atm X X X X X X40 Bar X X X X X X

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

1 Kmin 5 Kmin 10 Kmin

Sheet1

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
GR1 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR2 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
GR3 1 Kmin 5 Kmin 10 Kmin
Atm X X X X X X
40 Bar X X X X X X
Page 57: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Pressurized TGA Apparatus

bull Pressures up to 100 bar bull Temperatures up to

1000degCbull Heating rates controlled

bull 1 to 60 Kminbull He used as sweep gasbull Buoyancy correction as

function of P T and dTdtbull 10 mg samples

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 58: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Characteristic Results and AnalysisRaw Data Data After Buoyancy Correction

1 bar

40 bar

Kerogen pyrolysis

Carbonate decomposition

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 59: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Buoyancy Correction

Buoyancy Correction

Empty Basket Basket with Oil Shale

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 60: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Calibration with Curie Point Metals

163

164

165

166

167

168

169

170

0 100 200 300 400 500 600 700 800 900

TemperatureDeg C

Mas

sm

g

Iron 7587(7700)

Perkalloy5783(5960)

Nickel3450(3553)

Alumel 1394(1542)

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 61: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Oil Shale vs Kerogen

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 62: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Pyrolysis Models

64

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 63: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Kinetic Expressions

bull One major problem with non-isothermal analysis is how to mathematically treat the data

bull The equation to be solved has no analytical solution

bull Where H is the heating ratebull The problem is the exponent the comes as a result of

an Arrhenius rate expression

( )nTRE

mmeAHdT

mmd0

10 sdotsdotsdot= sdotminus

minus

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 64: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Modeling Approaches

bull Two general ways exist to regress the parameters from the equation

ndash Derivative method

ndash Integral method

int sdotminussdot= dT

TRE

HAmm )exp()ln( 0

( )TR

EE

TREHRA

T sdotsdotminus

sdotsdot

minussdotsdotsdot

=

minusminus

3221log1loglog 102

1010

α

TRE

HAdtd

sdotminus

=

minusαα

1

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 65: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Kinetic regression methods and resulting parameters for pyrolysis

bull Many common regression methods require linearized data

bull The methods are sensitive to data and lead to very different predictions for similar samples

bull Literature is lacking statistical confidence of determined parametersndash Part of the statistical problem is the methods used

to regress the data

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 66: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Author(s) Sample Experimental Setup

Heating Rate(s) (Kmin)

A(s-1)

E (or Eavg)(kJmol)

σ (kJmol)

Abu-Qudais et al(2005) Attarat (Jordian) Oil Shale TGA 35102040 792ndash917 ~

Ahmad and Williams(1998)

Kark and Salt Range (Pakistan) Oil Shale TGA 20 68-11058-93 ~

Avid et al(2000) Khoot (Mongolian) Oil Shale TGA 92 9628 ~Benbouzid and Hafsi(2008) Bitumen TGA 51020 100E+03 37-97 ~

Braun(1992) GROS Pyrolysis TQ-MS 110 5E+13 221 Not

availableBurnham(1991) GROS Pyromat II 00332 11E+15 241 58Campbell et al(1980) Green River () Oil Shale TGA 2 149219 ~

Dogan and Uysal(1996) Turkish Oil Shale TGA 20 125-434 ~

Jaber and Probert(2000)

Ellujjun and Sultani (Jordan) TGA 20304050 -100E-5 to -193E-3 39-68 ~

Koumlk and Pamir(2003)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 10 24-57 ~

Koumlk and Iscan(2007)

Can Mengen and Himmetoglu (Turkish) Oil Shales TGA 5 131ndash2154 ~

Li and Yue(2003) Chinese Oil Shale and GROS TGA 5 160-200 (dominant rxns) ~

Linder et al(1983) Swedish Oil Shale TGA 35-213 130 ~

Rajeshwar(1981) GROS TGA 51020 980E+10 116-209 ~Shih and Sohn(1980) GROS TGA 1-5 563E+11 197 ~

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 67: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Problems with These Modeling Approaches

bull Derivative ndash The noise in the data get amplifiedndash Even if significant smoothing is employed the derivative is

sensitive to the differentiation of α wrt Tbull Integral

ndash Numerically complexndash Approximations required to solve equation

bull Bothndash Extrapolate to 1T = 0 for interceptndash Incorrect for proper statistics

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 68: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Illustration of Challenges

Derivative

bull Noise

bull Endpoints

Integral

bull Noise

bull Endpoints

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 69: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Hillier Approach (BYU)

bull Solve the integral numericallybull Write a program to incorporate both the mass trace

and the derivativebull For the same sample and pressure conditions fit

simultaneously multiple (three in my case) heating rates with replicates

bull Optimize Parameters with an optimization routinendash Simulated Annealing then GRG (Generalized Reduced

Gradient)

bull Collect statistics on the kinetic parameters

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 70: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Simulated Data

bull One graduate student picked 4 pairs of A amp Endash Used first-order model to generate data at a

certain heating ratendash Added noise during the simulation

bull Second graduate student tried to recover the same values of A amp Endash Using methods in the literaturendash Using the new method (mass plus derivative)

72

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 71: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Can Method Reproduce Coefficients from Noisy Simulated Data

175

0

176

7

183

8

185

0

190

1 207

0

115

5

119

7 137

6

145

7

159

E+15

233

E+15

131

E+16

178

E+16

599

E+16

362

E+18

538

E+10

148

E+11

116

E+13

830

E+13

0

50

100

150

200

250

Actual NewMethod

Integral 10 pts

Integral 15 pts

Integral 25 pts

Integral All pts

Derivative10 pts

Derivative15 pts

Derivative25 pts

DerivativeAll pts

E a (k

Jm

ol)

100E+08

100E+09

100E+10

100E+11

100E+12

100E+13

100E+14

100E+15

100E+16

100E+17

100E+18

100E+19

100E+20

100E+21

100E+22

A (1

s)

Activation EnergiesPre-Exponential Factor

The new method performed better than standard techniques

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 72: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Models

74

( )010 mmeAH

dTmmd TR

E

sdotsdotsdot= sdotminus

minus ( )010 mmeAH

dTmmd TR

Eeff

sdotsdotsdot= sdot

minusminus

dEEE

XX E effint infinminus

minussdotminus

sdot=

2

22max 2

1exp2

1σσπ

First Order Distributed Activation Energy (DAEM)

where X is a conversion variable liketotal volatiles released

Eeff changes with conversion based on a normal distribution

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 73: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Pure Kinetic Modeling

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

Lots of variation in literature values of kinetic coefficients for pyrolysis of Green River oil shale

Fitting mass curve and derivative curve important for best curve fit

bull Tested vs ldquoartificialrdquo databull Other methods not quite as good

Good fits of data with first order and DAEM models

bull All heating rates fit with one set of constants

bull Experiments at pressure only changed Eact by 3 kJmol

Results from a Colorado oil shale

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 74: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Utah GR Kinetic Modeling(1 atm)

76

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 75: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Utah GR Kinetic Modeling(40 bar)

77

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 76: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

A and E values

78

Sample First-Order DAEM 1 atm 40 bar 1atm 40 bar

GR1

A (1s) 89E+13 28E+14 92E+13 10E+14 E (kJmol) 221 219 223 215 σ (kJ) -- -- 4 26

GR2

A (1s) 45E+13 80E+13 26E+14 30E+14 E (kJmol) 2169 210 2281 2194 σ (kJ) -- -- 26 67

GR3

A (1s) 95E+13 15E+14 94E+13 35E+14 E (kJmol) 220 217 222 225 σ (kJ) -- -- 46 53

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 77: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Very Little Effect of Pressure

79

0

50

100

150

200

250

1 atm 40 bar 1atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

GR1

GR2

GR3

1st Order DAEM

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 78: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Little Effect of Particle Size(when crushed)

80

0

50

100

150

200

250

1 atm 40 bar

Activ

atio

n En

ergy

(kJ

mol

)

38 micron

75 micron

150 micron

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 79: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Confidence Intervals for A and E(Arsquo is a way to normalize A)

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 80: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Advanced Pyrolysis Models

82

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 81: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Advanced Isoconversional Model

bull Activation energy E is a function of the extent of conversion (X)

bull Form of E vs X curve generated directly from non-isothermal data

bull A correlated to Ebull Fits data from 7 different heating ratesbull Uncertainty generated from data

83

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 82: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

84

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 83: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

bull Originally developed for coal pyrolysisbull Based on chemical structure of coal

ndash Lattice of aromatic clusters connected by aliphatic bridges

ndash Thermal decomposition of aliphatic bridges chops up the lattice

ndash Percolation statistics relate fraction of broken bridges to the amount of separated fragments

ndash Vapor pressure used to determine if fragments evaporate

ndash Large fragments crosslink back into the char

CPD model(Chemical Percolation Devolatilization)

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 84: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Representative Hydrocarbon Molecule

CH2

OH

R

R

CH2 CH2 CH2

R

CH2

O

CH3

COOH

R

CH3

Loop

Aromatic Clusters

BridgesSide Chain

bull Aromatic clustersbull Bridgesbull Side Chains

bull Bridges break during heatingbull Lattice statistics tell fraction of

detached clustersbull Vapor pressure determines if

detached cluster will evaporate

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 85: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

The Chemical Percolation Devolatilization (CPD) Model

Includesbull NMR for coal structurebull Chemical mechanism for bridge scissionbull Percolation lattice statisticsbull Vapor pressure modelbull Crosslinking

Predicts tar and light gas yields as a function ofbull Coal typebull Heating ratebull Temperaturebull Pressure

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 86: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Reaction Chemistry

bull Aromatic clusters do not break apart at pyrolysis temperaturesndash Normal coal pyrolysis temperatures are

~900-1000 K at high heating ratesndash Aromatic compounds break apart at gt2500 K

bull Bridges between clusters break during pyrolysisndash Not all bridges break (or else no char)ndash Crosslinking (new stable bridges formed)

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 87: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Bridge Scission Mechanism

pound pound2δ 2g1

c + 2g2

kb

kg

kc

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 88: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

How Does Bridge-Breaking Relate to Mass Release

Lattice structure (also called network)

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 89: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Types of Lattices

H O N E Y C O M B L A TTI C E TR I G O N A L B E TH E L A TTI C E

D I A M O N D L A TTI C E TE TR A G O N A L B E TH E L A TTI C E

A Coordination number = 3

B Coordination number = 4

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 90: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Relationship Between Broken Bridges and Finite Clusters

a p = 01 b p = 08

c p = 055 finite fragments d p = 055 infinite lattice

bull 20 broken bridgesbull 3 of 900 in fragments

bull 45 broken bridgesbull 100 of 900 in fragments

bull 90 broken bridgesbull 900 of 900 in fragments

image1wmf

a p = 01

b p = 08

c p = 055 finite fragments

d p = 055 infinite lattice

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 91: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Closed-Form Solution of Percolation Lattice Statistics

10

08

06

04

02

00

Frac

tion

of F

inite

Clu

ster

s

100806040200

Fraction of Intact Bridges (p)

4

126

σ + 1 = 3

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 92: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Vapor-Liquid Equilibrium and Crosslinking

Finite Fragments (Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

image1wmf

Finite Fragments

(Metaplast)

Infinite Coal Matrix

Tar Vapor

Reattached Metaplast

Crosslinking

Vapor-Liquid

Equilibrium

Labile Bridge Scission

MW

f

MW

f

MW

f

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 93: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

How Do You Treat Vapor Pressures of Coal Fragments

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 94: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model

001

01

1

10

100

Vapo

rPre

ssur

e(a

tm)

30252015

1000Temperature (K-1)

110MW = 315 285

258

218

237212

188 158 140127 116

(500 K)(667 K) (400 K) (333 K)

( )TMWccP ci

vapi exp 3

21 minus=

Data taken from Gray et al (Ind Eng Chem Process Des Dev 1985) for12 narrow boiling point fractions of coal liquids from a Pittsburgh seam coal

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 95: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Vapor Pressure Model Compares Well with Pure Component Data

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 96: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Input Parameters Required by the CPD Model

bull Number of attachments per cluster (σ+1) (ie coordination number)

bull Fraction of attachments that are bridges (p0) (bridgesbridges+side chains)

bull Molecular weight per aromatic cluster (Mcl)bull Molecular weight per side chain (Mδ)

bull Fraction of bridges that are stable (c0)

Mea

sure

d by

NM

R

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 97: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Other Parameters(not usually adjusted)

bull Rate coefficientsndash Assumed to be coal-independentndash Set based on extensive comparisons with datandash Uses sequential (not parallel) distributed activation energyndash Ab Eb σb Ag Eg σg Acr Ecr ρ (ratio of 2 Arsquos)

bull Vapor pressure coefficientsndash Assumed to be coal-independent

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 98: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Bridge Variables

100

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 99: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Good Agreement with Tar and Total Volatile Yields

70

60

50

40

30

20

10

0

Pred

icte

d Yi

eld

(CPD

)

706050403020100

Measured Yield

Maximum Tar Total Volatiles

Coal-dependent input coefficients taken directly from NMR datafor 16 coals (05 to 1000 Ks 1000 to 1300 K)

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 100: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

CPD Model Applied to Kerogen

Initial CPD Model CalculationsIdeabull Use chemical structure data

as inputs to pyrolysis modelbull Determine kinetic

coefficientsndash Bridge breakingndash Light gas releasendash Crosslinking

bull Adjust for distribution of chain lengthsndash CH4 CO etcndash Long-chain alkanes

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 101: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Why Try the CPD Model

bull Inputs based on chemical structurebull Applicable to different hydrocarbon without

changing rate coefficientsbull Predicts tar char and light gasbull Tar MW predictedbull Predicts effects of pressure on tar distributionbull Publicly available

103

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 102: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Conclusion

bull We are developing great toolsndash Chemical structure of bitumen kerogen and

pyrolysis productsndash Molecular modelsndash Simple pyrolysis kineticsndash Advanced pyrolysis kinetics

bull Chemical structure effectsbull Pressure effects

ndash Coupled with heat and mass transfer models

BYU College of Engineering amp Technology

Chemical Engineering

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 103: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

Acknowledgments

Thanks to James Hillier Trent Hall and Ryan Gillis

Major Referencesbull Hillier J L ldquoPyrolysis Kinetics and Chemical Structure

Considerations of a Green River Oil Shale and its Derivativesrdquo PhD Dissertation Chemical Engineering Department Brigham Young University (April 2011)

bull Hillier J L and T H Fletcher ldquoPyrolysis Kinetics of a Green River Oil Shale Using a Pressurized TGArdquo Energy amp Fuels 25 232-239 (2011)

bull Hillier J T Bezzant and T H Fletcher ldquoImproved Method for Determination of Kinetic Parameters from Non-isothermal TGA Datardquo Energy amp Fuels 24 2841-2847 (2010)

105

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106
Page 104: Oil Shale: Structure and Reactionstom/oilshale/THF...Shale Gas vs Oil Shale • Shale gas – From fracking – Largely natural gas – Some oil comes up with gas • Oil Shale –

106

  • Oil Shale Structure and Reactions
  • My Family
  • My Research Low Grade Fuels
  • Shale Gas vs Oil Shale
  • Slide Number 5
  • World Oil Shale Reserves
  • Utah Colorado Wyoming
  • Proven World Petroleum Reserves(165 trillion barrels)
  • Petroleum vs Oil Shale Reserves
  • Industrial Approaches
  • AMSO Process (Total)
  • Slide Number 12
  • Eco-Shale Process (RedLeaf)
  • Slide Number 14
  • So Why Arenrsquot We Producing Oil from Oil Shale
  • Objective
  • Skyline 16 Core
  • Kerogen vs Bitumen inGreen River Oil Shale
  • 2D Kerogen Model
  • Slide Number 20
  • Slide Number 21
  • Slide Number 22
  • Bitumen Analysis
  • Slide Number 24
  • Slide Number 25
  • Slide Number 26
  • Slide Number 27
  • Slide Number 28
  • Representative Hydrocarbon Molecule
  • Slide Number 30
  • Reactions
  • Kerogen Retort
  • Kerogen Retort Data
  • Kerogen Retort Data
  • Oil Shale vs Kerogen
  • Slide Number 36
  • Structure Parameters of Char
  • Lattice Parameters of Char
  • One problem with NMR analysis
  • Aromaticity of Char
  • Changes in Lattice Structure of Char
  • MW of Cluster and Side Chain(from Colorado Green River Oil Shale sample)
  • Carbon-13 NMR of Tars
  • Carbon-13 NMR of Tars
  • GCMS Analysis of Tars
  • GCMS Analysis of Tars
  • Alkane and Alkene Generation
  • FTIR Gas Analysis
  • FTIR Gas Analysis
  • Application to Chemical Percolation Devolatilization (CPD) Model
  • Summary and Conclusions
  • Summary and Conclusions(continued)
  • Summary and Conclusions(continued)
  • Acknowledgment
  • Thank You
  • 13C NMR Analysis of Kerogen Chars
  • 13C NMR Analysis of Kerogen and Chars from Kerogen Pyrolysis
  • Thermo-Gravimetric Analysis
  • Pressurized TGA Apparatus
  • Characteristic Results and Analysis
  • Buoyancy Correction
  • Slide Number 62
  • Oil Shale vs Kerogen
  • Pyrolysis Models
  • Kinetic Expressions
  • Modeling Approaches
  • Kinetic regression methods and resulting parameters for pyrolysis
  • Slide Number 68
  • Problems with These Modeling Approaches
  • Illustration of Challenges
  • Hillier Approach (BYU)
  • Simulated Data
  • Can Method Reproduce Coefficients from Noisy Simulated Data
  • Models
  • Pure Kinetic Modeling
  • Utah GR Kinetic Modeling(1 atm)
  • Utah GR Kinetic Modeling(40 bar)
  • A and E values
  • Very Little Effect of Pressure
  • Little Effect of Particle Size(when crushed)
  • Confidence Intervals for A and E(Arsquo is a way to normalize A)
  • Advanced Pyrolysis Models
  • Advanced Isoconversional Model
  • Slide Number 84
  • CPD model(Chemical Percolation Devolatilization)
  • Representative Hydrocarbon Molecule
  • The Chemical Percolation Devolatilization (CPD) Model
  • Reaction Chemistry
  • Bridge Scission Mechanism
  • How Does Bridge-Breaking Relate to Mass Release
  • Types of Lattices
  • Relationship Between Broken Bridges and Finite Clusters
  • Closed-Form Solution of Percolation Lattice Statistics
  • Vapor-Liquid Equilibrium and Crosslinking
  • How Do You Treat Vapor Pressures of Coal Fragments
  • Generalized Hydrocarbon Vapor Pressure Correlation for the CPD Model
  • Vapor Pressure Model Compares Well with Pure Component Data
  • Input Parameters Required by the CPD Model
  • Other Parameters(not usually adjusted)
  • Bridge Variables
  • Good Agreement with Tar and Total Volatile Yields
  • CPD Model Applied to Kerogen
  • Why Try the CPD Model
  • Conclusion
  • Acknowledgments
  • Slide Number 106