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 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
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
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 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
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
kδ
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
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
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
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
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
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 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
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
kδ
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
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
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
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
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
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 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
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
kδ
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
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
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
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
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
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 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
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
kδ
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
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
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
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
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
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 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
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
kδ
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
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
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
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
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
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 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
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
kδ
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
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
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
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
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
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 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
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
kδ
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
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
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
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
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
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 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
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
kδ
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
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
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
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
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
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 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
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
kδ
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
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
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
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
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
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 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
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
kδ
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
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
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
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
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
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 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
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
kδ
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
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
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
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
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
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 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
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
kδ
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
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
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
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
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
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 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
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
kδ
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
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
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
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
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
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 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
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
kδ
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
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
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
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
Objective
bull Determine the changes in macromolecular structure of oil shale kerogen during pyrolysisndash Three samples taken from a core in the Uintah
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
- 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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
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
kδ
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
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
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
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
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
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
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
kδ
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
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
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
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
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
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
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
kδ
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
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
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
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
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
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
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
kδ
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
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
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
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
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
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
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
kδ
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
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
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
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
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
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
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
kδ
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
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
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
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
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
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
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
kδ
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
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
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
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
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
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
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
kδ
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
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
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
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
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
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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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 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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
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
kδ
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
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
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
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
Bridge Scission Mechanism
pound pound2δ 2g1
c + 2g2
kb
kδ
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Conclusion
bull We are developing great toolsndash Chemical structure of bitumen kerogen and
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
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
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