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Product characterization of fast

pyrolysis of dry distillers grains withsolubles and palm kernel cake using a

heated grid reactor

Nitrogen chemistry and reactor modeling

MSc Thesis

Report number 2404

Delft University of TechnologyFaculty of Mechanical, Maritime and Materials Engineering (3mE)

Energy Technology

J. Gout

May 17, 2010

Professor Prof. dr. ir. A.H.M. VerkooijenSupervisor Dr.ir. W. de JongDaily supervisor J. Giuntoli, MSc


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Summary

Due to climate change, dependence on fossil fuels, growing imports and risingenergy costs, the importance of renewable energy sources in Europe is contin-uously growing. The use of biomass is one of the ways to increase the shareof renewable sources, since biomass can be used for heat and power productionand the production of transport fuels. Biomass can be converted directly intopower and heat by thermal conversion, or indirectly by converting it to gaseousor liquid fuel. In all of these processes, pyrolysis plays an important role. Py-rolysis or devolatilization is the process in which organic materials are heatedin an inert atmosphere to produce organic condensable vapors, gases and char-coal. The organic vapors can be condensed to produce pyrolysis oil that canbe further upgraded to transportation fuels. Also in the combustion process,pyrolysis is the first step in the release of volatiles. This step accounts for up to95% of the weight loss during combustion of biomass[1].

The purpose of this study is to gain more insight into the pyrolysis behaviorof DDGS and PKC under fast heating rate conditions relevant to industrialapplications like gasification. The aspects that are investigated are the volatile

yields and composition, mainly focusing on NOx precursors like NH3 and HCN.For this purpose, the measurements were carried out on an experimental setuppreviously described and used by Di Nola [1] and Tamboer[2]. The parametersthat were varied are temperature, heating rate and holding time. Also, the effectof leaching in water on the pyrolysis behavior of biomass is addressed. Becausethe possibilities for monitoring the reactor temperatures are limited, a CFDmodel was made. This model can provide a better insight in the phenomenaconcerning heat transfer and fluid flow within the reactor.

The parameters that were varied during the experiments are peak temper-ature, heating rate and holding time. The temperatures were varied between500 and 1300C, for the heating rate, 600 and 1000K/s were chosen. Finally,the holding time was varied from 5 to 15 seconds.

Non-contact measurements using a pyrometer showed that the actual grid

temperature is significantly higher than indicated by the thermocouple. A rela-tion between these temperatures was presented.

From the modeling it was concluded that there are not hot spots in thereactor other than surrounding the grid. Also, the gradient of the temperature islimited in the center of the grid, but substantial towards the electrodes. Finally,in a first approximation of the thermal history of the sample appeared to differsignificantly from that of the grid.

In the experimental section the issue of temperature difference between thegrid and thermocouple was taken into account. The results show that the be-havior of both potential fuels have a similar behavior in terms of weight loss

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and volatile product composition as a function of temperature. The leachingin water does not significantly influence the weight loss and volatile product

composition as a function of temperature. When comparing with low heatingrate experiments performed by Giuntoli et al.[3], the yields differ significantlyfor the species CO2, H2O, NH3 and HCN.

The main NOx precursor found in this study was HCN, while no HNCO wasfound. In PKC, NH3 was found in quantities that were too small to quantify.The yields of both NH3 and HCN were found to increase with temperature.The lower yields in NOx precursors compared to low heating rate pyrolysis issuggested to be caused by the reduced hydrogenation of char-N.

For future work on the presented CFD model, it is recommended to improvethe model by including relations for the thermal behavior of the pyrolysis sam-ple. Also, the release of volatile species could be incorporated in the model toprovide a better insight in the mixing behavior and possible secondary reactions.

On the experimental part, it is recommended to quantify the amount ofadsorbed NH3 and investigate whether other components such as H2O and HCNmight also adsorb. On the temperature, it is recommended to use the followingrelation to predict the actual grid temperature: TGrid 1.15 TThermocoupleFinally, to reduce the difference in temperature between the sample and thegrid, thinner samples should be used.

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Acknowledgements

Hereby I want to thank the people that supported me during this final project.First of all Jacopo, my daily supervisor. From the start on the experiments tothe writing of my report, you have been very helpful in guiding me and givingme useful feedback. Duco Bosma I want to thank for helping me to get familiarwith the heated grid setup. I also would like to thank my supervisor Wiebren forthe useful feedback during our progress meetings and off course Prof. Verkooijenfor giving useful comments on my work.

Off course I also want to thank my colleague students in the departmentthat were working on their (final) project during my final project. I had a goodtime enjoying your company during lunch and coffee breaks.

Jeroen Gout

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Nomenclature

Symbols

a Speed of sound [m/s]An Frequency factor for species n [s1]

C Concentration [ppm] or [kg m3]Cp Heat capacity [kJ kg

1 K1]d Sample diameter [mm]

DAB Binary diffusion coefficient [m2 s1]

De Effective diffusivity [m2 s1]

H Heat of reaction [J kg1]En Activation energy for species n [J mol

1]F Body force vector [N m3]I Identity matrix [-]J External current density [A m2]k Thermal conductivity [W m1 K1]

kn Reaction constant for species n [s1

]m Weight [mg]

Ma Mach number [-]n Normal vector [-]p pressure [Pa]Q Heat production W m3

Qj Current source [A m3

qs Production/absorption coeffcient [W m3 s1]

r Particle radius [m]t Time [s]T Temperature [K] or [C]ts Sample thickness [mm]u Velocity vector [m s1]

u Velocity in x-direction [m s1]v Velocity in y-direction [m s1]V Electric potential [V]w Velocity in z-direction [m s1]

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Greek symbols

Thermal expansion coefficient [T1] Ratio of specific heats [-] Emissivity [-] Dynamic viscosity [Pa s] Dilatational viscosity [Pa s] Density [kg m3]

c Restriction factor [-]e Electric conductivity [S m

1] Tortuosity [-]

p Pellet porosity [-]

Subscript

amb ambienta.r. As received basisd.b. Dry basis

d.a.f. Dry and ash free basisd Downu Up

Constants

g Gravitational acceleration 9.81 [m s1]R Universal gas constant 8.314 [J K1 mol1] Stefan-Boltzmann constant 5.6704 [W m2 K4]

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Abbreviations

CFD Computational fluid dynamicsDDGS Dry distillers grains with solublesDKP DiketopiperazineFTIR Fourier transform infrared spectrometerHHV Higher heating valueHR Heating rateHT Holding time

PKC Palm kernel cakePKE Palm kernel expeller

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Contents

1 Introduction 111.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111.2 Biomass residues . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

1.2.1 DDGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.2.2 PKC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

1.3 NOx emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151.4 Literature on nitrogen in biomass . . . . . . . . . . . . . . . . . . 161.5 Leaching in water . . . . . . . . . . . . . . . . . . . . . . . . . . . 191.6 Ob jectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201.7 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2 Experimental setup 222.1 Literature review on fast pyrolysis equipment . . . . . . . . . . . 222.2 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.2.1 Pyrometer . . . . . . . . . . . . . . . . . . . . . . . . . . . 232.3 Experimental procedure . . . . . . . . . . . . . . . . . . . . . . . 26

2.3.1 Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 262.3.2 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . 262.3.3 Running the experiments . . . . . . . . . . . . . . . . . . 272.3.4 FTIR sampling . . . . . . . . . . . . . . . . . . . . . . . . 27

2.4 Experimental considerations . . . . . . . . . . . . . . . . . . . . . 282.5 Setup improvements . . . . . . . . . . . . . . . . . . . . . . . . . 312.6 Discussion and conclusions . . . . . . . . . . . . . . . . . . . . . . 34

2.6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3 Modeling 353.1 Model dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.2 Heat transfer modeling . . . . . . . . . . . . . . . . . . . . . . . . 35

3.2.1 Subdomain equations . . . . . . . . . . . . . . . . . . . . 383.2.2 Boundary equations . . . . . . . . . . . . . . . . . . . . . 39

3.3 Conductive media modeling . . . . . . . . . . . . . . . . . . . . . 393.3.1 Subdomain equations . . . . . . . . . . . . . . . . . . . . 393.3.2 Boundary equations . . . . . . . . . . . . . . . . . . . . . 40

3.4 Weakly compressible Navier Stokes modeling . . . . . . . . . . . 403.4.1 Subdomain equations . . . . . . . . . . . . . . . . . . . . 403.4.2 Boundary equations . . . . . . . . . . . . . . . . . . . . . 41

3.5 Mesh properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413.6 Results & validation . . . . . . . . . . . . . . . . . . . . . . . . . 43

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3.6.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433.6.2 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.7 Biomass temperature modeling . . . . . . . . . . . . . . . . . . . 533.8 Release of volatiles . . . . . . . . . . . . . . . . . . . . . . . . . . 563.9 Discussion and conclusions . . . . . . . . . . . . . . . . . . . . . . 57

3.9.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . 58

4 Experimental results 594.1 Analyzing the results . . . . . . . . . . . . . . . . . . . . . . . . . 594.2 The influence of the final temperature . . . . . . . . . . . . . . . 59

4.2.1 Weight loss . . . . . . . . . . . . . . . . . . . . . . . . . . 604.2.2 Slow vs. fast pyrolysis . . . . . . . . . . . . . . . . . . . . 614.2.3 Nitrogen species . . . . . . . . . . . . . . . . . . . . . . . 624.2.4 H2O, CO, CO2 and CH4 . . . . . . . . . . . . . . . . . . 654.2.5 Partitioning . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.3 The influence of the holding time and heating rate . . . . . . . . 694.4 Discussion & conclusions . . . . . . . . . . . . . . . . . . . . . . . 72

4.4.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . 72

5 Conclusions and recommendations 745.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 745.2 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Appendices 82

A Biomass properties 83

B Quantification method 86

C Reactor dimensions 98

D Model settings 102D.1 Mesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102D.2 Constants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104D.3 Scalar expressions . . . . . . . . . . . . . . . . . . . . . . . . . . 104D.4 General heat transfer . . . . . . . . . . . . . . . . . . . . . . . . . 104

D.4.1 Subdomain . . . . . . . . . . . . . . . . . . . . . . . . . . 104D.4.2 Boundary settings . . . . . . . . . . . . . . . . . . . . . . 104

D.5 Conductive media . . . . . . . . . . . . . . . . . . . . . . . . . . 105D.5.1 Subdomain settings . . . . . . . . . . . . . . . . . . . . . 105D.5.2 Boundary settings . . . . . . . . . . . . . . . . . . . . . . 105

D.6 Weakly compressible Navier Stokes . . . . . . . . . . . . . . . . . 105D.6.1 Subdomain settings . . . . . . . . . . . . . . . . . . . . . 105D.6.2 Boundary settings . . . . . . . . . . . . . . . . . . . . . . 105

D.7 Solver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105D.7.1 Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 105D.7.2 Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

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E Devolatilization results 109E.1 Influence of the final temperature . . . . . . . . . . . . . . . . . . 109

E.2 Influence of the holding time . . . . . . . . . . . . . . . . . . . . 111E.3 Influence of the heating rate . . . . . . . . . . . . . . . . . . . . . 115

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Chapter 1

Introduction

1.1 Background

Due to climate change, dependence on fossil fuels, growing imports and risingenergy costs, the importance of renewable energy sources in Europe is contin-uously growing. Already in 1997 the European Union set a target for 12%renewable energy in gross inland consumption to be achieved 2010[4], however,this target was unfortunately not met. The current suggested target is setto 20% renewable energy by 2020. This target is also adopted by the Dutchgovernment[5].

The use of biomass is one of the ways to increase the share of renewablesources, since biomass can be used for heat and power production and the pro-duction of transport fuels. According to the Platform Biobased Raw Materials,

30% of fossil raw materials in the Netherlands could be replaced by biobasedmaterials in 2030[6]. This will require a replacement of 60% of transportationfuels, 20% of production of chemicals materials, 17% of heat production and25% of electricity production.

Biomass can be converted directly into power and heat by thermal conver-sion, or indirectly by converting it to gaseous or liquid fuel. In all of theseprocesses, pyrolysis plays an important role. Pyrolysis or devolatilization is theprocess in which organic materials are heated in an inert atmosphere to produceorganic condensable vapors, gases and charcoal. The organic vapors can be con-densed to produce pyrolysis oil that can be further upgraded to transportationfuels. Also in the combustion process, pyrolysis is the first step in the release ofvolatiles. This step accounts for up to 95% of the weight loss during combustionof biomass[1].

There are, however, some drawbacks for biomass. Unlike coal, which hasbeen studied extensively for already quite some time[7], the thermal behaviorof biomass has only been under investigation for a comparatively short time.Also, because of the diversity of available biomass, the difference in compositionbetween the different types of biomass is very large. One of the compoundsthat can vary in amount between different types of biomass, is nitrogen. Thiscompound can be found in amounts of less than 1% up to about 10 wt% on dryand ash free basis[1]. The problem of this fuel bound nitrogen is that it causesthe formation of the harmful NOx emissions. Since the amount and origin of

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nitrogen in biomass differs significantly, it is difficult to predict the amount ofNOx and N2O emissions in combustion processes. Besides the nitrogen, biomass

also contains significant amounts of alkalis like potassium, sodium, chloride andsulfur, which can cause fouling and slagging as well as corrosion of thermalconversion equipment[8].

To overcome the issues raised in the previous paragraph, research on the thepyrolysis of biofuels is necessary since this is the first step in the combustionprocess. In this work, the product yields and composition of volatiles releasedduring pyrolysis for two types of biomass were investigated as a function oftemperature, heating rate and holding time. The obtained results can providea better understanding of the thermo-chemical conversion, which can be usedto design processes involving pyrolysis, gasification or combustion.

1.2 Biomass residues

Biomass can generally be divided into four different classes[1]:

Wood and woody materials

Herbaceous and other annual growth crops (e.g. straw, grasses)

Agricultural by-products and residues (e.g. shells, hulls, pits and animalwastes)

Refuse-derived fuels (RDF) and non-recyclable papers, often mixed withplastics

In this study the focus is on agricultural by-products, namely dry distillers

grains and solubles (DDGS) and palm kernel cake (PKC). They are by-productsfrom bio-ethanol production from corn and the production of palm oil, respec-tively. Bio-ethanol is a first generation biofuel, since it competes with foodproduction. By using DDGS as a fuel, the efficiency of energy conversion fromcorn can be improved. Palm oil can be either used in food or a basis for biodiesel.Both types of biomass residues have become widely available, since they are be-ing produced on a large scale with production still increasing (see figure 1.1).They are currently used as animal feed because of their nutritional value andconsiderable protein content. Only recently, these types of biomass are beingconsidered as potential fuels[9, 3, 10], so the knowledge on pyrolysis behavior islimited.

1.2.1 DDGS

DDGS are a byproduct of dry grindethanol production from corn. This processaccounts for 67% of the production of ethanol from corn. In this process, cleancorn is mixed with water to form a mash. The mash is cooked and enzymesare added to convert starch into sugar. The next step is the addition of yeastto ferment the sugars, producing a mixture containing ethanol and solids. Thismixture is distilled and dehydrated to produce fuel-grade ethanol. The solidsremaining after distillation are dried to produce DDGS [13].

One of the possible uses of DDGS is to use it as a biofuel in ethanol plants,serving as an alternative for natural gas or coal. Tiffany et al.[9] made an

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(a) Palm oil production[11] (b) Ethanol co-product production[12]

Figure 1.1: Historic production data on palm oil and ethanol residues

economic model for the use of DDGS in 50 and 100 million gallons/year ethanolproduction plants. From this modeling, it appears that the energy content ofDDGS is such that it can supply both the process heat and electricity needed forethanol production, while an energy surplus is available for power generationfor sale to the grid. When gasifying DDGS, the renewable energy ratio ofethanol production in these plants can be increased from 1.5 to 3.8 (definedas (Energy in ethanol + co-product energy + electricity to grid energy)/ fossilenergy input)[14]. Also, the well-to-wheel greenhouse gas emissions for ethanolcompared to gasoline can be reduced by 39% (from 100,000 to 60,000 CO2-equivalent grams per million Btu of fuel produced and used)[15] while usingDDGS for powering the ethanol production.

It is expected that with the increasing ethanol production, the amount ofDDGS produced will exceed the demand from the market. This is partly causedby the fact that the amount of DDGS that can be fed to some types of cattleis limited. This is caused by a form of fat present in DDGS, of which certain

types of livestock (dairy cows, swine and poultry) can only receive a limiteddaily intake[9]. The saturation of the DDGS market for cattle feed will lowerthe DDGS prices, favoring the use of DDGS as a fuel. For the base case in thework of Tiffany et al., already the rate of return for a biomass powered ethanolproduction plant is higher than for a conventional one.

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1.2.2 PKC

Palm kernel cake (PKC), also called palm kernel expeller (PKE), is a by-productfrom mechanical palm oil production. This process consists of six unit pro-cesses:(1) fruit bunch sterilization,(2) bunch threshing,(3) fruit digestion,(4)pressing,(5) clarification and centrifugation and (6) nut drying, cracking, andkernel recovery[16]. Most of the worlds palm oil is produced in Malaysia andIndonesia. In 2006 their combined production amounted to 31.8 million tonsof palm oil, 87% of the world production[11]. For every ton of oil palm fruitbunch that is put into the oil refining process, 0.012 tons of kernel are producedas solid waste[10]. Just like DDGS, PKC is mainly used as a feed for livestockbecause of its considerable protein content[17].

1.3 NOx emissions

The nitrogen contained in biomass causes the formation of nitrogen oxides dur-ing combustion. NOx include various compounds like NO and NO2. Thesecompounds play an important role in atmospheric reactions that create harm-ful particulate matter, smog and acid rain. These effects cost society billionsof dollars each year from illnesses and deaths[18]. NOx contributes to the for-mation of fine particles that can cause respiratory diseases. It can also causeeutrophication of water, which causes oxygen depletion and the degradation ofwater quality and aquatic flora and fauna. Furthermore, NOx can react withorganic chemical species to form toxic products[1].

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1.4 Literature on nitrogen in biomass

In the last years, several studies on nitrogen chemistry related to biomass andthe formation of NOx precursors have been carried out. An extensive literaturestudy on the N-chemistry related to biomass was presented by Becidan[19].Becidan stated that nitrogen in biomass is not only originating from proteins,as suggested in some studies. The protein in biomass can account for 30 up to90% of the nitrogen. However, other sources of nitrogen can be found in theform of non-proteins. These non-protein-N can be found in the form of:

Free amino acids and polypeptides

Non-protein amino acids

Nucleic acids and mononucleotides

Alkaloids

Inorganic-N

Chlorophyll

Quaternary-N

Other minor N-compounds

Studies on NH3 and HCN release have been performed in two ways: studieson biomass and studies on model compounds. Model compounds are chemicalcompounds that depict a single chemical functionality of N. The main classes ofstudied model compounds are amino acids/proteins and N-heterocycles as they

are two representative biomass N-functionalities.Studies on the pathways of decomposition have shown that most of NH 3

and HCN are not primary products of model compounds decomposition butrather products of secondary decomposition[20, 21, 22, 23]. Furthermore, thepyrolysis of proteins gives a variety of fragments, depending on the protein[20].This means that a multitude of reactions is involved.

For the two main groups of model compounds, the decomposition mech-anisms were studied by Becidan[19]. These groups are protein/amino acid-s/oligomers/polypeptides and N-heterocycles, as mentioned before. For thefirst group, five pathways are presented as shown in figure 1.2. The two mainprimary pathways are:

Dehydration through formation of cyclic amides

Decarboxylation with consequent amine formation

Three other, less important pathways are:

Intermediary formation of -lactam

Amide intermediaries formation

Cross-linking of proteins side groups to produce char-N and NH3

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Figure 1.2: Primary decomposition paths for amino acids[19].

The most important secondary reactions that follow on the primary path-ways are the following:

Cleavages of DKP

Reactions of primarily formed amines

Reactions of primarily and secondary formed -lactam

Reaction of primary nitriles from primary amines Reactions of N-containing char compounds

The mechanisms suggested for the N-heterocycles pyridine and pyrrole, twoimportant N-heterocycles, involve breaking of bonds in the ring and/or ruptureof the C-N bond followed by random cleavage/recombination of the resultingdiradical[24]. The suggested mechanisms usually consist of numerous steps (75for pyrrole mentioned in [25]) where HCN is formed through a sequence ofreactions and not during the initial steps[26]. A study on pyridinic- and pyrrole-type compounds [27] shows significant amounts of NH3. The presence of -OHgroups would increase the amount of NH3 at the expense of HCN[28]. Theinvolved mechanism is however unclear.

Model compounds can help in explaining the decomposition of biomass, how-ever only to a limited extend. Because of the interactions between N-compoundsand non N-compounds, the yields of biomass decomposition can not be ex-plained by only looking at the model compounds. From the work of Becidanand other literature on N-chemistry related to biomass, the following two tableswere made, stating the main NOx precursors found at low and high heatingrates:

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Table 1.3: Proximate and elemental analysis of evaluated biomass residues

(% dry basis) DDGS DDGS leached PKC PKC leachedProximate analysis1

Moisture2 8.9 8.5 7 9.2Volatiles 78.2 76.2 75.5 75.1

Fixed carbon 14.7 17 18.4 19.5Ash 7.1 6.8 6.1 5.4

Elemental analysis1

C 49.00 48.80 49.99 47.76H 6.34 6.32 6.01 6.05N 4.47 4.50 2.35 2.71S 0.43 0.44 0.53 0.38O 32.65 33.2 36.0 37.7

HHV [MJ/kg] 19.81 20.50 17.81 18.85

1.5 Leaching in water

Abother difficulty associated with the use of biomass is the problems associatedwith the ashes that are formed. The ashes can cause problems like deposition,slagging, fouling, sintering and agglomeration. The ashes consist of inorganiccompounds, for biomass mainly alkali metals like potassium in the form ofinorganic salts. These salts can be dissolved in the moisture or connected tocarboxylic or other functional groups. Biomass is also rich in elements likechlorine and sulfur, compared to wood fuels. One of the troublesome reactionsthat is associated with the ashes, is the formation of alkali silicates which havevery low melting points which can cause depositing on reactor walls[8].

The ash thermal behavior can be improved by using leaching in water asa pretreatment. This procedure was presented by Arvelakis et al.[8] and isused to reduce ash content of the biomass and lower the amount of the so-called troublesome elements like potassium, sodium chlorine and sulfur. Tosee whether this treatment influences the volatile product yield or composition,both untreated and leached samples were investigated in this study.

The DDGS and PKC used in this study were soaked into water for 24 hourswith a water/mass ratio of 44.4g/L and 88.8 g/L, respectively. After the watertreatment the biomass was dried. The effect of leaching in water on the biomassis limited, ss can can be seen in table 1.3. The leaching slightly increasedthe heating value of both fuels, this can be attributed to the reduction in ashcontent. Also, the amounts of Na, Cl and S are reduced in both fuels (table1.4). The potassium content is slightly reduced in PKC, in DDGS the decreaseis negligible. Since the ash content and amount of potassium remain virtuallyunchanged after leaching, the effect of this treatment is limited and this willprobably also show in the pyrolysis results.

1Performed with automatic procedure on a Carlo Erba EA 9010 manufactured by Fisons.2On as received basis

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1.7 Outline

In chapter 2 is introduced with a short literature review on fast pyrolysis equip-ment is presented, followed by a description of the used experimental setupand its components. Following, the experimental procedure is discussed andsome improvements are presented, both on quantification of the results as thetemperature measurement.

After the experimental setup is presented, a CFD model of the reactor is in-troduced in chapter 3. First the dimensions of the model are presented, followedby the incorporated phenomena. For each phenomenon, the used equations arediscussed. Finally, the results and validation with experimental data are pre-sented. After validation, the temperature and decomposition of the biomass areevaluated in some more detail.

In the following chapter, chapter 4, the results of the experiments are pre-sented and analyzed. For the different parameters of temperature, heating rateand holding time the volatile yields and compositions are evaluated. Specialattention is paid to the yield of the NOx precursors NH3 and HCN.

Finally, the conclusions and recommendations are drawn.

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Chapter 2

Experimental setup

In this chapter, the experimental setup is discussed. After a short introduction offast pyrolysis apparatuses, the experimental setup and procedure are presented.Also, some considerations and improvements on both the setup and procedureare discussed. Finally, a short discussion and conclusions on the chapter arepresented.

2.1 Literature review on fast pyrolysis equip-

ment

Fast pyrolysis experiments using a heated grid (also called a wire mesh or screenheater) reactor have been used before in different studies[37, 38, 39, 7, 40, 41,

42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 1, 58]. The advantageof this apparatus is that the time of pyrolysis can be precisely controlled and agood mass balance can be obtained. Both can be controlled within a few percent.Also, secondary reactions are minimized because the volatiles are released in arelative cold environment[7]. One of the drawbacks of the system is the fact thatthe temperature of the sample is not well-known and can not be easily measured.Also, the temperature and temperature distribution of the grid are not very wellknown[59]. The temperature of the grid can differ from the temperature of thethermocouple because of poor heat transfer or heat loss to the thermocouple.Because of the low heat capacity of the grid, also the temperature profile of thegrid or wire mesh can be non-homogeneous. Additionally, possible hot spots inthe reactor could cause secondary reactions.

For fast pyrolysis, also other equipment is available. The Curie-Point re-

actor and entrained flow reactor are capable of heating rates of 500-104

K/sand 5x104K/s, respectively[2]. In the Curie-Point reactor, the sample holderis heated by electro magnetic induction. Curie-Point heaters generally havethe same questions with respect to the ability of the ferromagnetic material totransfer heat to the sample[7] as for the heated grid.

In a drop tube furnace, the most widely used version of the entrained flowreactor, particles entrained in a carrier gas are injected along the axis of a hotfurnace tube into a flowing preheated gas stream[7]. There are a number ofadvantages of this technique. The reactor can provide high heating rates andhigh temperatures. Also, residence times are reasonably well controlled and

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known. A disadvantage of the technique is the fact that the released volatilesremain hot so secondary reactions will take place. Also, the temperature history

of the particles is not very well known.

Figure 2.1: Schematic top view of the heated grid reactor[60]

2.2 Experimental setup

The experimental setup used in this study, has been designed and used before byDi Nola[1] and Tamboer[2]. It consists of a heated grid reactor that is connectedto a Fourier transform infra red (FTIR) spectrometer. In figure 2.1 a schematicof the reactor is presented. In the reactor, a foil or wire mesh is supported bytwo electrodes. The grid is heated using resistive heating achieved by applyingan electric current. The current is controlled by a computer, which makes itpossible to control temperature and heating rate of the grid. The gases in thereactor are circulated by a pump through heated circulation lines. Both thereactor and the circulation lines are heated to avoid condensation of pyrolysisproducts and to limit the adsorption. The circulation lines connect the reactorto the gas cell, which is a cylindrical chamber with ZnSe windows on both sides.Through the gas cell, the volatile products are analyzed by the FTIR.

The FTIR in the setup is a Thermo-Nicolet NEXUS FT-IR, as shown infigure 2.2. For details on this apparatus, please see the work of Di Nola[1]. Thesample compartment is replaced with the heated grid reactor, while the gas cellis lined up with the detector mirror. The FTIR is continuously purged with

nitrogen to maintain an inert atmosphere. To cool the detector compartment,liquid nitrogen is used. The software used with the FTIR is the Thermo NicoletOMNIC software package version 6.1. This package provides software for controlof the FTIR and analyzing the obtained data. More details on the quantificationusing FTIR data can be found in appendix B.

The complete setup schematically looks like the representation in figure 2.3.

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Figure 2.2: Thermo-Nicolet NEXUS FTIR[60]

Figure 2.3: Heated grid setup overview[1]

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2.2.1 Pyrometer

The pyrometer used in the experiments for investigating the grid temperatureis an Impac IGA5 MB20 pyrometer. The pyrometer is mounted to the tableon which the FTIR with heated grid setup was standing. The pyrometer canbe precisely positioned in x and y direction since it is mounted on two 25mmtravel motion control translation stages, see figure 2.4. The pyrometer measuresthe temperature in a spot of 1.1mm diameter at a distance of 90mm. Themeasurement was done through the window of the reactor lid. This does notinfluence the measurement since this is 3mm thick BK7 glass, which is for 99.9%transparant for the used wavelength[61].

Figure 2.4: Pyrometer setup schematic

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2.3 Experimental procedure

Since the setup has been designed and used Di Nola[1] and Tamboer[2], anexperimental procedure has already been specified. However to ensure repro-ducibility of the experiments, the experimental procedure followed in this studywill be shortly discussed. Also, some optimizations were made during this study.

2.3.1 Parameters

The parameters that were varied during the experiments are peak temperature,heating rate and holding time. Table 2.1 shows the parameters that were usedin this study. The temperatures were varied between 500 and 1300C justlike in the work of Di Nola[1]. These temperatures cover the range at whichindustrial processes involving pyrolysis are operated. For the heating rate, 600

and 1000K/s were chosen to compare with the work of Di Nola. Finally, theholding time was varied from 5 to 15 seconds. The holding time of 15 secondswas chosen because the used sample weight in this study was 5-7 mg instead of3-5 mg, so there might be a bigger influence of heat transfer limitations. Sincethis appeared to be the case only at 500C, at higher temperatures a holdingtime of 10 seconds is used.

Heating rate [K/s]Temperature [C] 600 1000

500 5-10-15 10600 10 -700 10 -800 10 -900 10 -

1000 10 -1100 10 -1200 10 -1300 5-10-15 10

Holding time [s]

Table 2.1: Experimental parameters

2.3.2 Procedure

The first step in preparing for the experiments was preparing the samples. Thebiomass was pressed into slab type pills of 3 mm in diameter. The use of pillsinstead of powder was chosen to ensure a similar behavior in terms of heattransfer in every experiment. Also, for modeling of the pyrolysis and recoveryof the char a disc shape is more convenient. The untreated DDGS was deliveredin pellets, so these had to be ground into powder before being pressed into pills.This was done by hand with a pestle and mortar. The palm kernel cake (PKC)was delivered in powder form so no grinding was needed. The biomass waspressed into discs of 5-7 mg, which resulted in pills of about 0.7 mm thickness.This amount was chosen since it allowed for reasonably thin pills, while providing

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a sufficient amount of biomass necessary for the detection of minor species likeNH3 and HCN.

The second step was preparing the setup itself. The FTIR had to be filledwith liquid nitrogen to cool the detector. This was done at least 30 minutesbefore the experiments, to give the detector time to cool down. Also, the re-actor and the circulation lines had to be heated up. Thirty minutes before themeasurements, the heaters were set to 130 C.

2.3.3 Running the experiments

The experimental procedure that was used is the following:

Weigh the sample.

Place the sample on the grid.

Flush the reactor with helium for 2 minutes at 300ml/min.

Close the in- and outlet.

Collect a background measurement.

Start the 3 minute sample series collection.

Ten seconds after the series collection started, start heating of the sample.

When the series collection is complete, open the in- and outlet and retrievethe sample.

Weight the sample.

The samples were weighted on a scale with sensitivity of 0.01 mg, howeverthe sample weight was determined with an accuracy of 0.1 mg because of smallfluctuations.

2.3.4 FTIR sampling

For the FTIR sampling, the same settings were used as suggested by Tamboer[2].This means the mirror velocity was set to 1.8988 [m/s] at a resolution of 0.25cm1 and the number of scans per sample was set to three. This means everymeasurement takes 9 seconds, so every series consists of 21 samples.

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2.4 Experimental considerations

In the work of Di Nola[1], the volatile composition was determined immediatelyafter devolatilization. This was done to prevent secondary reactions. Thisapproach is questionable, since this probably does not allow for the volatiles tofully mix and reach a homogeneous gas composition. To see at which time thevolatile composition can be considered homogeneous, experiments were donewith decomposing CaCO3. During the decomposition of CaCO3, CaO and CO2are formed. Since this experiment was done in a helium atmosphere, secondaryreactions involving CO2 are not likely to occur. It appeared that the amountof detected CO2 remains quite constant after approximately two minutes, seefigure 2.5. Therefore it was decided to use the spectrum two minutes after thepeak in the CO2 concentration for the final quantification.

0 0.5 1 1.5 2 2.5 3 3.53500

3550

3600

3650

3700

3750

3800

3850

3900

3950

t[min]

Concentration[ppm]

Figure 2.5: CO2 concentration over time after CaCO3 decomposition at 1200C,heating started at t=0s.

For the detection of NH3 there is not only the issue of mixing but also ad-sorption on the reactor walls. Since the reactor is made of steel, it is very likelythat the reactor surfaces adsorb NH3 to some extent. From FTIR measure-ments on chicken litter, an ongoing decrease in time suggest that there is indeedadsorption (figure 2.4). To see to what extent adsorption is likely to occur,

the setup was flushed with 1%vol NH3 in helium. After the flushing, anothermeasurement on chicken litter was done. While measuring over time, after thefirst decrease in concentration another increase in concentration of NH3 wasobserved (figure 2.7). This suggests that there is indeed desorption of the NH3that was adsorped during the flushing.

To correctly quantify the amount of NH3 that was released during the ex-periments, the adsorption has to be taken into account. To do so, the release ofHCN was considered. Since both HCN and NH3 probably originate from similarcompounds, so the timescale for their release was considered to be similar. Thisassumption is supported by the fact that both NH3 and HCN show a similar

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0 0.5 1 1.5 2 2.5 3 3.550

0

50

100

150

200

250

t[min]

Concentration[ppm]

Figure 2.6: NH3 concentration over time, released from chicken litter at T =800C

HR=600K/s and HT=15s, heating started at t=10s.

0 0.5 1 1.5 2 2.5 3 3.50

50

100

150

200

250

300

350

t[min]

Concentration[ppm]

Figure 2.7: NH3 concentration over time after flushing with NH3, released fromchicken litter at T=800C HR=600K/s and HT=15s, heating started at t=10s.

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steep peak in the concentration(figure 2.8), unlike species like CO2 which arereleased much slower and show a much broader peak. So the trend from HCN

can be used to predict the trend without absorption of NH3. After two min-utes, the HCN concentration is approximately 70% of the peak value. The NH3concentration was thus calculated as 70% of the peak value.

0 0.5 1 1.5 2 2.5 3 3.50

0.2

0.4

0.6

0.8

1

1.2

t[min]

Fractionofmaximum

detectedconcentration

HCN

NH3

Figure 2.8: NH3 and HCN concentration over time, released after heating atT=1300C, HR=600 K/s and HT=10s, heating started at t=10s.

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2.5 Setup improvements

In the setup the temperature is measured by a thermocouple. The temperatureof the thermocouple can be lower than the grid temperature because of heat lossthrough the thermocouple surface or bad contact between the thermocoupleand the grid[59]. Besides these errors, the wiring of the thermocouple couldalso introduce an error. A thermocouple consists of a junction of two dissimilarmetals, which generates a voltage if the junction is heated. This phenomenon iscalled the Seebeck effect and occurs for all dissimilar metals[62]. In the heatedgrid setup an S-type thermocouple is connected to stainless steel connectors,which are in turn connected to thermocouple extension wire. This means thatthe circuit of the thermocouple consists of several dissimilar metal junctions.Since the reactor was kept at a temperature of 130C during the experiments,the connections between the thermocouple and the connectors to the extensionwire will probably show a significant Seebeck effect. This error was discoveredin the starting value of previous measurements, see figure 2.10.

Figure 2.9: Schematic of improved wiring.

To reduce the error introduced by the thermocouple wiring, the heated partof the thermocouple extension wires replaced by a piece of stainless steel weldingwire (figure 2.9). In this way, the connection between the extension wire and thestainless steel is at ambient temperature instead of the reactor temperature. Asa result, the initial temperature error was significantly lower. When the reactortemperature was set to 130C, the thermocouple gave an initial temperature ofabout 100C. A similar temperature was measured in the reactor with a K-typethermocouple. Before, the thermocouple gave an initial temperature of about300C when the reactor was set to 110C, see figure 2.10.

It has been suggested that measuring the temperature with a thermocouplemight introduce a significant error[59]. To obtain a more accurate value for theactual temperature on the grid surface, an infrared pyrometer (IMPAC IGA 5MB20, a=90mm) was used. When the emissivity of the surface to be measuredis accurately known, this device can give an accurate temperature measurementwithout influencing the thermal behavior of the surface. Since the surface emis-sivity increases with increasing temperature because of physical changes in thefoil, the foil has been heated for 1 minute at a set point temperature of 1100 Cin a Helium atmosphere. After this treatment, the surface has a light gray, dullappearance. The emissivity of this surface has been determined by heating the

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0 2 4 6 8 10 1 2 140

100

200

300

400

500

600

700

800

900

t [s]

T[C]

Tset

TTC

(a) This study (b) Previous work

Figure 2.10: A temperature curve from this study (left) and a previous studyon the same setup[63] (right)

foil with a ceramic heater to approximately 300C. At this temperature, thetemperature was determined with a 0.1 mm K-type thermocouple. The pyrom-eter has been pointed to this same spot and the emissivity has been adjustedto measure the same temperature. The surface appears to have an emissivity of0.75. It was assumed that the emissivity does not change significantly over thetemperature range used in the experiments, since no additional visual changesoccur after heating.

400 500 600 700 800 900 1000 1100 1200

500

600

700

800

900

1000

1100

1200

1300

1400

T set [C]

T[C]

Measured @ =0.75

Measured @ =1Measured @ =0.5

Set point

Figure 2.11: Measured temperature at different emissivities.

To approximate the real temperature during the experiments, the emissivitysetting of the pyrometer was set to 0.75 and the temperature was measured forset points ranging from 500 to 1100C. The result can be found in figure 2.11.To see the influence of an error in emissivity, the range of temperatures haswas measured with emissivity settings 0.5 and 1. It can be seen that even at

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emissivity of 1 (which will give the lowest possible temperature), the measuredtemperature was still 70-120C higher than that of the thermocouple. This

indicates that the thermocouple temperature is very likely to be too low. If theemissivity at higher temperatures is going towards 0.5, the temperature mightbe significantly higher than what was measured at emissivity of 0.75. This ishowever very unlikely since emissivity tends to increase with temperature.

The trend of temperature deviation between the thermocouple and the py-rometer appeared to be quite linear. To determine the relation between the twotemperatures, a linear function was fitted to the average measured temperature,see figure 2.12. This function was used to determine the set point for the setupduring the experiments. The set points were set in increments of 5C, see table2.2.

500 600 700 800 900 1000 1100500

600

700

800

900

1000

1100

1200

1300

Tset

[C]

T[C]

Tpredicted

=1.15*Tset

Measured

Figure 2.12: Relation between actual temperature and setpoint

Experiment temperature Set temperature500 435600 525700 610800 700900 785

1000 8701100 9601200 10451300 1135

Table 2.2: Temperature set points for experiments [C]

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2.6 Discussion and conclusions

In the description of the experimental setup, some improvements were suggested.One of the suggestions was to quantify the volatile yields after allowing thevolatile products to fully mix. The drawback of this approach is that secondaryreactions might occur and some species might be adsorbed in the system. ForNH3 it was considered very likely that adsorption takes place. The species HCNand H2O, however, show also a greater decrease over time than CO, CO2 andCH4 (approximately 30% and 10%, respectively). This can be caused by a fasterrelease mechanism, but could also be caused by adsorbtion.

2.6.1 Conclusions

From the evaluation of the experimental setup, the following conclusions can bedrawn:

When quantifying the volatile species in the heated grid setup, mixing ofthe volatiles has to be taken into account.

NH3 is likely to adsorb in the setup, this has to be considered in thequantification.

Changing the hot parts of the thermocouple connection wire from thereactor to the computer significantly reduces the measuring error.

The temperature of the grid is significantly higher than indicated by thethermocouple.

The actual grid temperature can be predicted using the relation:

TGrid 1.15 TThermocouple

.

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Chapter 3

Modeling

In this chapter, a CFD model will be discussed that was made during this study.First, the purpose of the model is presented, followed by the model geometry.Next, the equations used are discussed, and finally the validation with experi-mental data. After validation of the model, the temperature of the biomass isinvestigated using the model. This is followed by a suggestion for expandingthe model with the release of volatiles. Finally, the results of the modeling arediscussed.

The heated grid or heated wire mesh setup has been used before in numer-ous fast pyrolysis studies[1, 54, 58] (see chapter 2 for more references). It hasbeen recognized however, that there is a level of uncertainty in the temperaturespresent in both the grid and biomass sample[59, 7]. Because of the fast heat-ing rates and small dimensions, it is difficult to measure temperatures withoutinfluencing the system. To provide a better understanding of the temperaturesthat are present in the system, a CFD model was made.

The temperature of the heated grid is controlled using a fine wire thermo-couple. The temperature of this thermocouple can be lower than that of thegrid due to poor contact. Also, due to heat loss through the thermocouple, theactual grid temperature can be lower at the spot where the thermocouple is incontact with the grid. One of the purposes of the model is to gain a betterinsight in the actual temperature of the grid. Furthermore, this model can helpto detect possible hot spots in the reactor volume. The model can later on beused as a basis for modeling the release of volatiles in the experiments. Themodel was made using the COMSOL Multiphysics 3.5a software[64].

3.1 Model dimensions

In the model presented here, only the reactor itself was modeled. The systemboundary is shown in figure 3.1. The boundary was chosen because the condi-tions here are well known (wall temperature, inlet velocity and temperature).The model looks as in figure 3.2. The exact dimensions of the model can befound in Appendix C.

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Figure 3.1: System border for the physical model.

Figure 3.2: Overview of the reactor model.

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3.2 Heat transfer modeling

The model was built using three different modules to take into account thedifferent relevant phenomena. The following COMSOL modules were used:

General heat transfer

Conductive media

Weakly compressible Navier Stokes

The first module, general heat transfer, takes into account the three funda-mental heat transfer mechanisms: convection, conduction and radiation. Thefollowing assumptions were made for this module:

The reactor inlet temperature is constant over time.

The reactor wall temperature is uniform and constant over time.

The reactor lid temperature is uniform and constant over time.

Heat transfer through radiation is only significant for the grid.

The basis for these assumptions is discussed in the section that presents theboundary equations.

3.2.1 Subdomain equations

In the model, each volume is defined as a subdomain with its own equations.The heat transfer subdomain equation that is solved, is the following:

CpT

t+ (kT) = Q + qsT Cpu T (3.1)

Density [kg/m3]Where is the density [kg/m3], Cp is the specific heat capacity [kJ/kg*K],

T is the temperature [K], k is the thermal conductivity [W/m*K], Q is the heatsource [W/m3], qs is the production/absorption coefficient and u is the velocityvector [m/s]. The first left hand term takes into account the thermal storage inthe volume, the second term takes into account the conduction. On the righthand side, the first two terms account for heat production, the third one accountsfor convection. This equation is solved for each subdomain, the convection termis only used for the fluid subdomain and not for the solid subdomains.

Table 3.1: Boundary settings heat transferBoundary Equation

Inlet T = T0Outlet - n (kT) = 0External boundaries T = T0Internal boundaries - nu (kuTu) nd (kdTd) = 0Grid boundaries - nu (kuTu) nd (kdTd) =

T4amb

T4

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Table 3.2: Boundary temperatures.

Boundary Temperature [

C]Reactor wall 110Reactor lid 95Inlet 110

3.2.2 Boundary equations

The heat transfer boundary equations can be found in table 3.1. For the inlet,a temperature was set. The temperature at this boundary was assumed to beequal to that of the circulation line walls and was assumed not to change sig-nificantly during the experiments. The outlet boundary was set to convectiveflux, which means only convective heat transfer is possible at this boundary.

The external boundaries were also set to a constant temperature, because thereactor was kept at a constant temperature by heating elements and has a highthermal mass so the temperature will not change significantly during the experi-ments. The internal boundaries were set to continuity, except for the boundariesbetween the grid and the fluid. The grid temperature during the experimentsranges from 500 to 1300 C, so heat transfer by radiation was considered besignificant. The heat source/sinkboundary setting allows for the incorporationof radiation. The radiation from the grid was set to surface-to-ambient, whichimplies that there is no interaction with other surfaces in terms of radiation.This was done because the other surfaces were at relative low temperatures andthe surface area of the grid is small compared to the surrounding surfaces sothe effect of reflection was expected to be minimal. The equation used for thegrid boundaries looks as follows:

nu (kuTu) nd (kdTd) =

T4amb T4

(3.2)

Where n is the vector normal to the surface, is the surface emissivity and is the Stefan Boltzmann constant [5.6707e-8 W m2 K4].

The temperatures for the boundary settings were determined using a 0.1mmsheathed K-type thermocouple. The obtained temperatures are shown in table3.2.

3.3 Conductive media modeling

Since the grid is heated using resistive heating, the electric effects have to betaken into account. This module is only used for the grid and the connected

electrodes, where it calculates the heating as a function of the current. Thecurrent as a function of time in the model is the same as the input to the realsetup.


The used subdomain equation is the following:

(eV Je) = Qj (3.3)

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Where e is the electric conductivity [S m1], V is the electric potential, J

is the external current density vector [A m2] and Qj is the current source [A

m3]. From the current density, the heat production is calculated in the heattransfer module.


The boundary equations for the conductive media are very straightforward, sincethere is one inward current flow and one ground while the other boundaries areelectric insulation, see table 3.3.

Table 3.3: Boundary settings conductive mediaBoundary Equation

Electrode 1 bottom Jn = I(t)

Electrode 2 bottom V = 0Other boundaries n J = 0

3.4 Weakly compressible Navier Stokes model-

ing

The weakly compressible Navier Stokes module was used to calculate the fluidflow. This model is based on the incompressible Navier Stokes equation, buttakes into account the effect of the temperature on the density. The followingassumptions were made:

The flow is incompressible.

The flow is laminar.

Buoyancy effects can be accounted for by the simplified Boussinesq ap-proximation.

Since the equation is based on the incompressible Navier Stokes equation,it can only be used for low Mach numbers. Usually, Ma=0.3 is taken as alimit for incompressible flow[65]. At the outlet, the velocity is the highest andthus the Mach number will be at its maximum. The outlet velocity can becalculated from the volume flow which is known to be 2.6 l/min from the pumpspecifications and the cross sectional area which is 4.97e-5 m2. Assuming the

outlet to be at the reactor temperature, 110

C, the speed of sound in heliumis a = (RT)1/2 = (1.66 2077 383.15)1/2 = 1149, the Mach number at theoutlet of the model is[65]:

Ma =|u|

a=

0.87

1149= 7.6e 4 0.3 (3.4)

So the assumption of an incompressible fluid is valid.

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The weakly compressible Navier Stokes equation looks as follows:

u

t+ u u =

pI +

u + (u)T

(2/3 ) ( u) I

+ F (3.5)

t+ (u) = 0 (3.6)

Where u is the velocity [m s1], p is the pressure [Pa], the dilational vis-cosity [Pa*s], I the identity matrix and F the internal force [N m3]. For theBuoyancy effect, the Boussinesq approximation can be used. This approxima-tion replaces the continuity equation by the incompressible form[65]:

u = 0(3.7)

This approximation is valid when the flow can be regarded incompressible.However, also the temperature variations must be limited. The approximationcan be used when the following condition holds:

= T 1 (3.8)

Because of the high grid temperatures, this condition will not hold for theflow close to the grid. It was however not possible to use the compressiblecontinuity equation with the standard COMSOL equations. If necessary, thisequation could be implemented in the model later on. For now the Boussinesqapproximation was implemented as follows:

F = (0 ) g (3.9)


The boundary conditions for the weakly compressible Navier Stokes equationsare very straightforward. At the inlet, a uniform velocity was assumed. Thisdoes not represent the actual flow pattern, but this simplification will not sig-nificantly influence the overall solution . The velocity was calculated from thevolumetric flow rate of the pump which is assumed to be according to its spec-ifications. At the outlet, the pressure was specified. At other boundaries, noslip was assumed. The boundary settings are shown in table 3.4.

Table 3.4: Boundary settings weakly compressible Navier StokesBoundary Equation

Inlet u = - U0 nOutlet p = p0Other boundaries u = 0

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3.5 Mesh properties

For the meshing of the model, the meshing was done one sub domain at a time.Since the grid is a plate, the number of elements is by default very large. To re-duce computational time, the meshing for the grid was set to extremely coarse.Other subdomains were meshed with normal element size. To see whether thecoarse meshing of the grid can be justified, the model was solved in transientmode for both the coarse mesh and a refined mesh at a thermocouple tempera-ture of 500C. The number of elements in the grid and fluid, the most importantsub domains, are presented in table 3.5.

Table 3.5: Number of elements in the grid and fluid sub domainSub domain Elements in coarse mesh Elements in refined meshFluid 48081 78569

Grid 2620 10066

To judge whether the number of meshed elements is sufficient, a plot of boththe grid temperature and peak temperature over time were made, see figures3.3 and 3.4. The finer mesh shows a smoother temperature distribution, butthe coarse mesh appears to mesh with sufficient detail. Also, in the trend of thepeak temperature no significant difference can be identified. It was thereforedecided to use the coarse mesh.

(a) Coarse mesh (b) Finer mesh

Figure 3.3: Grid temperature distribution for coarse and finer mesh atTthermocouple=500C and t=10s.

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(a) Coarse mesh (b) Finer mesh

Figure 3.4: Peak temperature over time for coarse and finer mesh atTthermocouple=500

C.

3.6 Results & validation

3.6.1 Results

One of the most interesting results is the temperature distribution within thefoil. In the work of Prins et al.[59] the temperature distribution of a foil wasdetermined using a camera for recording visible emission. The result is shown in

figure 3.5. Please note that temperatures below 600

C have no physical meaningin this figure since this was outside the calibration range of the camera. Thepicture shows the temperature distribution of a 0.1 mm nickel/chromium foilof 10x16 mm while the temperature indicated by the thermocouple was 725C.While the foil used in the model in this study has different properties (0.05 mm18/8 steel 8x14 mm), the model was solved using a similar temperature to makea rough comparison. For this purpose the model was solved in steady state fora thermocouple temperature of 700C and a reactor temperature of 25C (sincethe reactor of Prins et al. was not at elevated temperature). The result is shownin figure 3.6.

If the distribution of the temperature within the two figures is compared,both show that the temperature at the center of the foil is reasonably uniform.The gradient towards the side in x-direction of the model appears to be larger,

which makes sense since the foil is thinner and thus has a lower heat capacity.Also the temperature of the electrodes in the setup of Prins et al. is unknown,which can influence the gradient. Another difference is the fact that the peaktemperature of the model is slightly below the thermocouple temperature, whilePrins et al. found a slightly higher temperature.

If we look at the temperature distribution in the y-z and x-z plane of thereactor (figures 3.7 and 3.8), the distribution of the temperature shows virtuallyno Buoyancy effects. Because of the low velocities, conduction is the governingphenomena close to the grid. There are however some Buoyancy effects visible,if we look at the arrow plot and velocities in the y-z plane, see figures 3.9 and

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Figure 3.5: Temperature distribution of the grid found by Prins et al. forTthermocouple=725

C .

Figure 3.6: Temperature distribution of the grid in the model forTthermocouple=700

C in steady state, Treactor=25C.

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Figure 3.7: Temperature distribution in the y-z plane of the reactor forTthermocouple=1000

C, HR=400K/s and HT=10s at t=10s.

3.10. Probably the Buoyancy forces actually are stronger than predicted by the

model, because the Boussinesq approximation is not valid close to the grid. Thecompressible Navier Stokes equation that should be used here is not availablewithin the COMSOL package.

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Figure 3.8: Temperature distribution in the x-z plane of the reactor forTthermocouple=1000

C, HR=400K/s and HT=10s at t=10s.

Figure 3.9: [v,w] velocity field arrows in the y-z plane for T thermocouple=1000C,

HR=400K/s and HT=10s at t=10s.

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Figure 3.10: Distribution of the velocity in z direction for the same conditionsof the previous plots.

3.6.2 Validation

To validate the results from the physical model in a quantitative way, the resultsfrom modeling were compared to what was measured by both a thermocoupleand a pyrometer. The pyrometer was positioned in such a way that it measuredthe spot temperature of the grid where the thermocouple is in contact with

the grid from below. This is at the center of the grid, where temperature wasconsidered to be the highest.

In figure 3.11 the temperatures measured by both the thermocouple andpyrometer are presented, as well as the current and prediction of the model. Oneof the first things that draws the attention is the peak in the model prediction.This is probably caused by the solver settings, since the predicted temperatureat higher temperatures also tends to be a bit instable (figure 3.12). Overall itappears that the model works quite well in terms of dynamic response, sincethe heating rate was predicted adequately. Please note that the line of thepyrometer only starts at 250C, since this is the lower limit of the operationalrange.

A plot of the temperatures measured by the pyrometer and thermocoupleover a range of temperatures is presented in figure 3.13, together with the modelpredictions. The temperature that was estimated at low temperature settingsis similar to what was measured by the thermocouple (see figure 3.11). Athigher temperatures, the temperature tends towards the trend measured bythe pyrometer. However, it is unlikely that the temperature indicated by thethermocouple is correct, since even if the grid were to have an emissivity of 100%,the temperature would still be significantly higher according to the pyrometer aswas shown in the previous chapter. Since the appearance of surface of the gridchanged after heating, it could be the case that also the temperature dependanceof the electric and thermal properties have changed.

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0 2 4 6 8 100

100

200

300

400

500

600

700

800

t [s]

T[C]

0 2 4 6 8 100

3

6

9

12

15

18

21

24

I[A]

(a) Temperature and current as a function of time.

0 2 4 6 8 100

100

200

300

400

500

600

700

t [s]

T[C]

Tthermocouple

Tpyrometer

Tmodel

(b) Measured and predicted temperature.

Figure 3.11: Temperature and current for the reactor with parametersT=500C, HR=600 K/s and HT=10s

0 2 4 6 8 10100

200

300

400

500

600

700

800

900

1000

t [s]

T[C]

Tthermocouple

Tpyrometer

Tmodel

(a) T=800C.

0 2 4 6 8 100

200

400

600

800

1000

1200

1400

t [s]

T[C]

Tthermocouple

Tpyrometer

Tmodel

(b) T=1100C.

Figure 3.12: Measured and predicted temperatures for the reactor with param-eters T=800C (left) and T=1100C (right), HR=600 K/s and HT=10s

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At higher temperatures (>800C) the predictions of the model are quiteclose to the findings from the pyrometer. Only at the set point of 1100C, the

model is starting to overestimate. This is probably caused by the fact that mosttemperature dependent properties were only defined up to 1200-1400K and lin-ear extrapolation is used here. Since the predictions at higher temperatures arereasonably accurate, probably the Boussinesq approximation does not introducea significant error in this range.

400 500 600 700 800 900 1000 1100 1200400

500

600

700

800

900

1000

1100

1200

1300

1400

Tset

[C]

T[C]

Tset

Tpyrometer

Tmodel

Figure 3.13: Trend for the measured temperatures and model predictions

Figure 3.14: Lines where the temperature is determined

Besides the peak temperature, also the distribution of the temperature overthe surface was investigated. Since the pyrometer gives a spot measurementof the temperature, the distribution had to be determined using an array ofmeasurements. The temperatures over the x and y centerlines of the grid weredetermined as shown in figure 3.14. This was done after the grid had been atthe set temperature for a few minutes to ensure that the grid and its electrodeswere not significantly changing over time. The results are given in figures 3.15and 3.16.

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Apart from the temperature difference, the temperature profile at 500C cor-responds quite well with the experimental values. Only close to the electrodes,

the gradient in the model is higher. This is however not very important, sinceonly the center of the grid is used for pyrolysis experiments. If we look at thetemperature distribution at 800C, the distribution in x direction correspondsquite well but in y direction the experimental results show an eccentric trend,this was also visually observed in some experiments. This is probably caused bybad contact at the electrodes as was also suggested by[59]. This phenomenonoccurred in some of the experiments and proved difficult to avoid. However sincethe gradient in y direction is limited, the influence on the pyrolysis temperatureis small.

8 6 4 2 0 2 4 6 8

x 103

200

250

300

350

400

450

500

550

600

x[m]

T[C]

Model

Measured

(a) T-x distribution.

4 3 2 1 0 1 2 3 4

x 103

200

250

300

350

400

450

500

550

600

y[m]

T[C]

Model

Measured

(b) T-y distribution.

Figure 3.15: Grid temperature distribution over x and y for set point T=500C

8 6 4 2 0 2 4 6 8

x 103

300

400

500

600

700

800

900

1000

x[m]

T[C]

Model

Measured

(a) x

4 3 2 1 0 1 2 3 4

x 103

300

400

500

600

700

800

900

1000

y[m]

T[C]

Model

Measured

(b) y

Figure 3.16: Grid temperature distribution over x and y for set point T=800C

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Table 3.6: Biomass sample properties

variable value unitd 3 mmts 0.7 mm 1200 kg m3

k 0.07 W m1

Cp 1200 J kg1 K1

0.75 -

3.7 Biomass temperature modeling

In the heated grid setup used in this study, the temperature of pyrolysis was mea-sured on the grid. This temperature will differ from the average biomass tem-

perature to some extent. However, measuring the temperature of the biomasswith a thermocouple will influence the temperature of the biomass, since thesample and thus its heat capacity is very small. Measuring the temperature ina non-contact measurement with a pyrometer is also difficult, due to the factthat the emissivity changes during pyrolysis and infrared absorbing gases areemitted that would influence the measurement. By making a CFD model ofthe biomass, more information about the biomass temperature can be gainedwithout the previous mentioned problems.

Modeling the biomass can be done in the CFD model of the reactor that wasmade. One of the difficulties when modeling the thermal behavior of biomass isthe fact that the thermal properties are not well known and that they are a func-tion of temperature and conversion rate. To make a first approximation of thetemperature in the DDGS during pyrolysis, some assumptions on the thermal

properties were made. The thermal conductivity k for DDGS was obtained fromthe work of Rosentrater[66]. The density was calculated by using the mass andvolume of the samples from the experiments in this study. The heat capacityis unknown, so this has to be estimated. Since DDGS does not differ too muchfrom wood in terms of density and elemental composition, the heat capacityof DDGS is assumed to be similar to wood. For wood, the heat capacity canrange from approximately 1.2 to 2.3 kJ/kg*K, depending on moisture contentand temperature[67]. To get a first impression of the temperature distributionduring pyrolysis, a biomass sample with a heat capacity of 1.2 kJ/kg*K andone with a heat capacity of 2.3 kJ/kg*K were modeled. For the emissivity ofthe sample, = 0.75 was assumed. This value was also used by Ragland et al.calculations on the combustion of wood[68]. The emissivity was specified at thecontact surface with the fluid and is specified as radiation to ambient, just aswas done for the grid. The contact between the sample and grid was assumed tobe ideal, so the boundary condition was specified using the continuity equation.The properties of the sample are presented in table 3.6.

As can be found in figure 3.17, the temperature difference between thebiomass average and grid peak is significant. Also the holding time is sig-nificantly shorter for the modeling with the higher heat capacity. This indicatesthat the thermal history of the sample can not be taken to be the same as thatof the grid. However to determine the thermal history of the sample with moreaccuracy, improvement on the modeling is necessary.

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0 2 4 6 8 10 12 140

200

400

600

800

1000

1200

t [s]

T[C]

Biomass volumetric average

Grid peak

(a) Cp=1.2kJ/kg*K

0 2 4 6 8 10 12 14

200

400

600

800

1000

t [s]

T[C]

Biomass volumetric average

Grid peak

(b) Cp=2.3kJ/kg*K

Figure 3.17: Peak and (volumetric) average biomass temperature over time forTthermocouple=1000C, HR=600K/s and HT=10s.

The model for biomass temperature prediction can be improved using amethod similar to the one in the work of Damartzis et al.[58]. In the work ofDamartzis, the pyrolysis of biomass was modeled using the particle approach.This method takes into account both heat transfer and reaction kinetics, makingthe thermal properties dependent on the degree of biomass conversion. The usedequation in this study looks as follows:

(CBcPB + CCcPC ) Tt= 1

r2

rkr2 T

r+ (H) dCB

dt(3.10)

Where C is the concentration [kg m3 ], cp is the heat capacity, T is thetemperature [K], r is the particle radius [m] and H is the heat of reaction [Jkg1]. The subscripts C and B refer to the char and biomass, respectively. Thisequation is a spherical form of the energy equation that is also solved in thephysical model that was made in this study. The term for biomass conversionis defined as:

dCBdt

= k1CB k2CB (3.11)

Where k1 and k2 are the kinetic rate constants for the rate of formation ofgaseous compounds (volatiles and tar) and char, respectively. The kinetic rate

constants are defined using the Arrhenius equation:

k1 = A1eE1/RT

quationWhere A is the frequency factor [s1], E is the activation energy [J mol1], R

is the gas constant [J mol1 K1 and T is the temperature [K]. The parametersfor this equation, the frequency factor A and activation energy E, can be ob-tained from fitting the relation of char formation as a function of temperature.The relations for the thermal properties are obtained from literature.

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3.8 Release of volatiles

The release of volatiles from a biomass sample could be a useful extension tothe model that was made during this study. Assuming that the sample temper-ature can be accurately predicted using the modeling approach as described inthe previous section, the release of volatile species can be described as a functionof temperature within the sample. The kinetics for the species can be obtainedfrom low heating rate experiments as presented by Guintoli et al.[3]. While theformation of volatile species can be described by the Arrhenius equation, thedistribution of these species in the solid and gas can be modeled using the Con-vection and diffusion module is COMSOL. The binary diffusion coefficients formost species can be determined as a function of temperature using Appendix Din the book of Turns[69]. The effective diffusivity in the solid can be determinedusing the relation as presented by Fogler:

De = DABpc

(3.13)

Where De is the effective diffusivity, DAB is the binary diffusion coefficient,p is the pellet porosity, c is the restriction factor and is the tortuosity[70].The pellet porosity can be calculated using the relation given in the work ofDamartzis et al.[58], the restriction factor and tortuosity can be taken as thecommon values suggested by Fogler.

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3.9 Discussion and conclusions

From the quantitative validation (figures 3.12 and 3.13), it appears that themodel is able to predict the peak temperatures and heating rate quite well fortemperatures between 900 and 1200C. Also, the temperature distribution iswell approximated for both low(500C) and higher(800C) temperatures (figures3.15 and 3.16). Assuming that the thermocouple was placed correctly in themiddle of the grid, there was no significant influence of heat loss through thethermocouple according to the pyrometer measurements. This hints towardsthe conclusion that the thermocouple gives a lower temperature because of badcontact between the thermocouple and the grid. This is in agreement with thesuggestion of Prins et al.[59] that a foil will be less influenced in terms of heatloss through the thermocouple.

From the grid (figure 3.6) and reactor temperature distribution (figure 3.7),it appears that the physical model behaves quite well. Also, there are no hotspots visible other than near the grid that would cause secondary reactions totake place. It could however be that secondary reactions take place in this zoneimmediately after release or when circulating the volatiles. The latter is howevernot very likely to have a big influence since the total reactor volume is circulatedabout two times within the holding time. The fraction of the gas that will gothrough the hot zone in this time will be limited.

The Buoyancy effect in the model is calculated using the Boussinesq approx-imation. Since this approximation is only valid for incompressible flows witha limited gradient in temperature, the use of this approximation is not com-pletely valid. Since the temperature deviation at higher temperatures is lower,it appears that the approximation can be used.

For improving on the modeling of the biomass temperature, the modeling

approach of Damartzis et al.[58] can be used. This approach was presented tomodel fast pyrolysis of a particle, but because of the small sample size it probablycan be applied for modeling the sample in this study without introducing a largeerror. The approach of Damartzis et al. allows for the thermal properties ofbiomass to be calculated as a function of temperature and conversion. Theconversion can be calculated as presented in the section on devolatilization inthis study. The modeling approach of Damartzis et al. can be used to obtainthe kinetics for weight loss as well.

In the work of Damartzis et al., the model for fast pyrolysis of a particleappears to correspond to experimental data quite well. The approach was, how-ever, significantly different from what is presented in this work, since Damartzistakes the biomass temperature as a starting point. The basis of this study wasto question the temperature of the biomass in the first place. In the work of

Damartzis, the biomass consists of a 200 mg layer in a folded wire mesh wherethe thermocouple measures the bulk temperature. It can be questioned whetherthis is a valid starting point because it is known that the grid itself already hasa temperature gradient.

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3.9.1 Conclusions

Evaluating the results of the modeling, a number of important conclusions canbe drawn:

There are no hot spots in the reactor other than around the grid.

Only in the center of the grid the gradient in temperature is low, so correctplacement of the sample is important.

According to a first approximation, the temperature and heating rate ofthe sample differ significantly from the grid.

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Chapter 4

Experimental results

In this chapter, the results of the fast pyrolysis experiments will be discussed.First, the quantification method is discussed briefly. Then, the influence ofthe temperature on the volatile composition and yields is discussed. This alsoinvolves the comparison between slow and fast pyrolysis. Following, the influenceof holding time and heating rate is investigated.

4.1 Analyzing the results

The volatile yields of the experiments are quantified using Quant Pad soft-ware, part of the OMNIC 6.1 software package. For the quantification, a cal-ibration method has been defined to determine the quantities for each speciesfrom the infrared absorption lines obtained with the FTIR. The description of

this method can be found in appendix B. This quantification method gives thevolatile amounts in ppm. To relate the volatile yields to the sample weight, ithas to be converted to mg. This can be done using the densities and reactorvolume. The densities are calculated using the ideal gas law. The equation forthe conversion to mg looks as follows:

m [kg] = C[ppm] V

m3 [kg/m]

The reactor volume V is 200ml, the density is calculated at the initial reactorconditions which are a pressure p 1.013 bar and the temperature T=110C.

The weight loss and species yields are presented of dry and ash free basis(d.a.f.) except for H2O, which is presented on as received basis. The d.a.f.weight can be calculated as follows:

md.a.f. [mg] = ma.r.

100 moisture [wta.r.%]

100

100 ash [wtd.b.%]

100

(4.1)

Now the d.a.f. yield for species i is calculated using:

Y ieldi,wt%,d.a.f. =mi

msample,d.a.f.

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4.2 The influence of the final temperature

In this section the influence of the final temperature on the volatile yields andcomposition is evaluated. Please note that the temperatures are those aftercorrection using the fitted relation presented in chapter 2.

4.2.1 Weight loss

400 600 800 1000 1200 14008085

90

95

100

105

Weightloss[wt%d.a.f.]

400 600 800 1000 1200 14008085

90

95

100

105

400 600 800 1000 1200 140080

85

90

95

100

Temperature [C]

Weightloss[wt%d.a.f.]

400 600 800 1000 1200 140080

85

90

95

100

105

Temperature [C]

Figure 4.1: Weight loss as a function of the final temperature. Clockwise,starting from top left: DDGS, leached DDGS, leached PKC and PKC.

The influence of the final temperature on the weight loss is quite similarfor each type of biomass, as can be seen from figure 4.1. Both the differencebetween the two types of biomass and their leached and untreated state does notseem to significantly influence the weight loss. This is to be expected since theirelemental composition is quite similar. Giuntoli et al.[3] also found no effectof the leaching on the weight loss characteristics. At 1300C, for the DDGS itappears that the weight loss is still increasing while for PKC it appears the finalyield has been reached at about 86-87%. The shape of the weight loss curves issimilar as found by Damartzis et al.[58] for olive residues, although the yieldsand temperatures differ.

The weight loss found for DDGS is higher than what was found by Giuntoliet al., who found a weight loss of 78-79% for untreated and leached DDGS at900 C. The weight loss found in this study is about 83% at 900 C, increasingup to about 87% at 1300 C. According to the weight loss curve of slow pyrolysisof DDGS, the primary devolatilization of DDGS should be complete at about600C[3]. It is possible that during the slow pyrolysis, some volatile compoundshave condensed in the char at lower temperatures and that they start decom-posing at higher temperatures. For fast heating rates these compounds will bereleased at once, giving a higher weight loss.

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The spread in the measurements is probably caused by uncertainty in theweight determination and difficulties with char recovery. The error that is in-

troduced by weighing is about 0.8% (on an initial weight of 6 mg), since thescale weighs the weight in 0.01 mg but it was read to 0.1 mg. The error in thechar recovery is difficult to quantify, but the recovery will tend to be slightlytoo low because of residues left on the grid. The recovery appears however tobe reasonably well since overall virtually no residue was left on the grid.

4.2.2 Slow vs. fast pyrolysis

In table 4.1 the yield of DDGS in slow pyrolysis, from the work of Giuntoliet al.[3], and fast pyrolysis are compared. Please note that the yields for CO,CO2, NH3 and HCN are given before isothermal heating. In the slow heatingrate experiments, the setup is kept at 900C for 60 minutes after the initialheating. The mentioned species show an additional release when kept at 900Cfor a long time. This can be attributed to a secondary devolatilization step,associated with decomposition of the rearranged structure of the solid[71]. Sincethis process is limited by mass transfer, this step will not take place during fastpyrolysis. It i

product characterization

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