Biomass undBiorMrgy Vol 7 Nos 1-6 ppl07-124 1994

BIicvicr Science Ltd Printed in GnIat Britain

0961-9534194 $700+000

MEASUREMENT AND MODELING OF LIGNIN PYROLYSIS

MICHAEL A Sma SYLVIE CHARPBNAY ROSEMARY BASSILAKlS Alm PETER R SOLOMON

Advanced Fuel Research Inc 87 Church Street East Hartford CT 06108 USA

Abstract-Pyrolysis of lignin is one approach that has been investigated to upgrade this material into higher value products However there have been relatively few efforts to quantitatively model these reactions This paper describes a methodology for modeling lignin pyrolysis which has been extensively developed for related materials like coal The samples are characterized using pyrolysis experiments under a standard set of conditions where the products are analyzed by Fourier Transform Infrared (FIshyIR) Spectroscopy and Field Ionization Mass Spectrometry (FIMS) Solvent extraction experiments are done to determine the clttractables yields and elemental analysis is done to further constrain the model One lignin produced from ethanolwater extraction of mixed hardwoods was selected for the application

of this modeling approach The model was able to qualitatively predict the tar molecular weight distributions and quantitatively predict the variations of the gas and tar evolution rates and yields with heating rate for the calibration set of experiments The model can be improved by more precise information on lignin structure crosslinking chemistry and tar transport mechanisms It also needs to be validated by simulation of pyrolysis conditions at high heating rates andor high pressures for which data is currently not available

Keywords--Lignin pyrolysis modeling FfIR FIMS structure

INTRODUCTION

There is an abundance of lignin residues produced as a by-product of pulp and papershymaking which have a fuel value of between $000 and $004 per pound The size of this resource can be appreciated by considering that the total amount of Iignosulfonates and Kraft lignin together outweigh the sum of all man-made polymers in the United States2

Given the relatively low cost high abundance and renewable nature of this resource it is not surprising that many attempts have been made to develop higher value products from lignin134

Pyrolysis of lignin is one approach that has been investigated to upgrade this material into higher value products- However there have been relatively few efforts to quantitatively model these reactions7- 12 Nunn et aI utilized a single first-order reaction scheme Klein and Virks developed a lignin pyrolysis simulashytion for a native spruce lignin which was approximated as an ensemble of single ring

phenolic units Both the lignin structure and the pyrolysis of the lignin were described probabilistically and the reaction kinetic parameters were obtained from model comshypound pyrolyses This model was extended by Petrocelli and Klein to simulate Kraft lignin pyrolysis Because of the changes in lignin structure which occur during the Kraft pulping process the model was changed to accommodate a wider variety of side chains and inter-unit linkages In more recent work Train and Klein used a Monte Carlo approach to simulate a lignin structure and its subsequent thermal decomposition Again the kinetic parameters were determined from corresponding model compound reactions and the effects of mass transfer limitations and catalysis were included in the model This methodology has been further refined and applied to the pyrolysis of heavy hydrocarbons in a recent paper

The approach that Klein and coworkers81l-i3

have followed utilizes fundamental rate conshystants for classes of reactions based on model

107

108 M A SERIO et al

Fig 1 Schematic of TG-FTIR apparatus

compound information andor thermochemical calculations The structure of lignin is also described in a fundamental way using statisshytics A more phenomenological approach to the problem of lignin pyrolysis has been taken in this paper and in previous work by Soloshymon and coworkersmiddot101416 which focuses on observed evolution rates of major products rather than the many individual reactions which lead to the evolution of a given product from a complex macromolecule such as lignin A vni et aI used a functional group (FG) model to describe gas evolution from several lignins and found that the gas evolution rates were similar and close to the results observed for low-rank coals The evolution of tar is treated in a semi-empirical fashion in the FG model by dividing the lignin into tar forming and non-tar forming fractions which have a similar composition to the parent lignin A more sophisticated model for tar evolution from lignin was developed by King et al 1017

which approximated lignin as a distribution of oligomers which were allowed to undergo random cleavage of weak bonds with transport of depolymerization fragments by vaporization and diffusion to predict product yields and composition This model was later improved to include a Monte Carlo solution technique and crosslinking reactions which are related to gas evolutions and the model was called the Depolymerization Vaporization and Crosslinkshying (DVC) model The use of a Monte Carlo solution technique to describe the depolymerization process is a common feature of the approach of Klein and coworkersampmiddotll12 and Solomon and coworkersmiddotJOI4-1amp In more recent work on coal the gas and tar evolution models were combined into a single model called the FG-DVC model This paper describes the application of the FG-DVC

model to lignin pyrolysis

EXPERIMENTAL

Sample selection

A sample of ALCELLTtd (ALC) lignin was obtained from Repap Technologies Inc of Valley Forge PA This is an experimental lignin made from mixed hardwoods by an organosolv process using aqueous ethanoL A limited amount of work was done on a steam explosion lignin (SEYP) obtained from Proshyfessor Wolfgang Glasser of the Virginia Polytechnic Institute This lignin was preshypared from Yellow Poplar wood Both the ALC and SEYP lignins were selected since they were part of the International Energy Agency (lEA) round robin analysis of lignins (20) which meant that detailed analytical information would be forthcoming on both lignins While the results of the lEA study were published after this modeling effort was complete it does provide a good foundation for future work on modeling these lignins

The ALC lignin was selected over the SEYP lignin for the detailed modeling effort since it has the potential to be available in large quantities and at low cost as a by-product of a novel pulping process under development by Repap21 However the same characterization methods were used for the SEYP lignin to help provide a basis for generalizing the model for other types of lignins

TG-FTIR Analysis

A series of programmed pyrolysis experishyments was done in a TG-FTIR (Thennogravishymetric Fourier Transform Infrared) system developed at Advanced Fuel Research

109 Measurement and modeling of lignin pyrolysis

Fig 2 TOmiddotFTIR Analysis of ALe lignin at 30degC min-I up to 9O()OC (a) Timetemperature profile balance and sum of tar and gases (b) tar (c-t) gases

(AFR)22 The apparatus consists of a sample suspended from a balance in a gas stream within a furnace A schematic is shown in Fig 1 Its components are as follows a DuPont 95 I TGA a hardware interface (inclushyding a furnace power supply) an Infrared Analysis 16 pass gas cell with transfer optics a Michelson 110 FTIR (resolution 4 cmmiddot detector Men A helium sweep gas (250 cm3 SmiddotI) is employed to bring evolved prodshyucts from the TGA directly into the gas cell A window purge of 700 cm3 S-I was employed at each end of the cell The system is opershyated at atmospheric pressure

As the sample is heated the evolving volshyatile products are carried out of the furnace directly into as cm diameter gas cell (heated to ISOdegC) for analysis by FTIR The FTIR spectrometer can obtain spectra every 02 s to determine quantitatively the evolution rate and composition of several hydrocarbon comshypounds The samples were heated using a pie-programmed temperature profile at rates of 10deg30deg and 100degC min-I up to a temperature

of about 900degC The system continuously monitors (1) the time-dependent evolution of the gases (including specific identification of the individual species such as ca lt~ ltH4bull ltHz ~Ha CH30H benzene etc) (2) the heavy liquid (tar) evolution rate and its infrared spectrum with identifiable bands from the functional groups and (3) weight of the non-volatile material (residue)

As an example of the TG-FIIR analysis procedure the pyrolysis of the ALC llgnin at 30degC min-l is described Figure 2a illustrates the weight loss from this sample the sum of the evolved products and the temperature history A 20 mg sample is taken on a 30degC min-I temperature excursion in the helium sweep gas first to 150degC to dry for 4 min and then to 900degC for pyrolysis The evolution rates of gases derived from theIR absorbance spectra are obtained by a quantitative analysis program that employs a database of integrashytion regions and calibration spectra for differshyent compounds Figures 2b-f illustrate the evolution rates and integrated amounts

110 M A SIIUo et al

10 80 100 Heating Rate (OCmin)

Fig 3 Effect of heating rate on tar yield from ALe lignin

evolved for tar CH H20 CO and CO respectively Because the data are quantitatshyive the sum of the evolved products matches the weight loss as determined by the TGA balance (Fig 2a) The sum ofevolved prodshyucts also includes minor products such as CH30H which are not shown in Fig 2 By comparing the peak positions for different species and how these shift for different heating rates kinetic information can be obtained on the individual product evolutions

At a heating rate of 30degC min-I in the TGshyFTIR and atmospheric pressure the tar yield was 46 dry wt for the ALC lignin and 36 dry wt for the SEVP lignin The amount of tar formed was sensitive to the heating rate and to the bed depth The effect of the heatshying rate on the tar yields from ALC lignin is shown in Fig 3 The effect of bed depth was not studied systematically but a fixed bed reactor experiment with a-ISO mg sample of ALC lignin packed in a 07 cm ID tube gave only -36 dry wt tar vs 46 dry wtOlo in the TG-FrJR under similar heatingconditions14

bull1s

The results suggested that the highest yields of tar would be obtained in a dilute phase entrained flow pyrolysis experiment This is similar to what has been observed in the case of the pyrolysis of coal2l and for whole biomasS24

Elemental analysis

The ALe lignin was subjected to elemental analysis at Huffman Laboratories (Golden CO) The results are given in Table 1 and compare well with those obtained in the IEA round robin study This is a direct input into the model since the predicted product evolshyution is constrained by the elemental composishytion of the starting material

FIMS Analysis

The ALC lignin was sent for analysis in the pyrolysis-Field Ionization Mass Spectrometry (FIMS) apparatus at SRI International This technique is described by St John et 0115 The apparatus consists of an activated tantalum foil field ionizer interfaced with a 60 magnetic sector analyzer and a PDP 11123 computer for data acquisition and processing Approximateshyly 50 ~g of the sample is introduced via a heatable direct insertion probe Mass spectral data of the evolving volatiles are collected by repeatedly scanning the magnet over a preset range while the sample is gradually heated at a constant rate The sample holder is weighed before and after the analysis to determine the volatile fraction

The field ionization induces little fragmentashytion and so provides a determination of the pyrolyzates molecular weight The FIMS analyses were done using a programmed pyrolysis at -3degC min-J ofthe material into the inlet of the mass spectrometer (-270 Pa) The mass spectra were taken at regular intershyvals so that the evolution of individual comshypounds could be tracked as a function of temperature A sample of the SEYP lignin was run under similar conditions for comparishyson

The FIMS spectra (Fig 4) show that the two lignins yield similar products upon pyrolysis This feature had been observed in previous studies of eight other lignins9bullIO14 The main difference was in the evaporation curves (Fig 5) where the ALC lignin was found to volatilshyize at significantly lower temperatures which can be explained by the fact that it contains more low molecular mass products

The relative intensities of the peaks were also different between these two samples the main peak in ALC lignin is 332 amu while

110 M A SIIUo et al

10 80 100 Heating Rate (OCmin)

Fig 3 Effect of heating rate on tar yield from ALe lignin

evolved for tar CH H20 CO and CO respectively Because the data are quantitatshyive the sum of the evolved products matches the weight loss as determined by the TGA balance (Fig 2a) The sum ofevolved prodshyucts also includes minor products such as CH30H which are not shown in Fig 2 By comparing the peak positions for different species and how these shift for different heating rates kinetic information can be obtained on the individual product evolutions

At a heating rate of 30degC min-I in the TGshyFTIR and atmospheric pressure the tar yield was 46 dry wt for the ALC lignin and 36 dry wt for the SEVP lignin The amount of tar formed was sensitive to the heating rate and to the bed depth The effect of the heatshying rate on the tar yields from ALC lignin is shown in Fig 3 The effect of bed depth was not studied systematically but a fixed bed reactor experiment with a-ISO mg sample of ALC lignin packed in a 07 cm ID tube gave only -36 dry wt tar vs 46 dry wtOlo in the TG-FrJR under similar heatingconditions14

bull1s

The results suggested that the highest yields of tar would be obtained in a dilute phase entrained flow pyrolysis experiment This is similar to what has been observed in the case of the pyrolysis of coal2l and for whole biomasS24

Elemental analysis

The ALe lignin was subjected to elemental analysis at Huffman Laboratories (Golden CO) The results are given in Table 1 and compare well with those obtained in the IEA round robin study This is a direct input into the model since the predicted product evolshyution is constrained by the elemental composishytion of the starting material

FIMS Analysis

The ALC lignin was sent for analysis in the pyrolysis-Field Ionization Mass Spectrometry (FIMS) apparatus at SRI International This technique is described by St John et 0115 The apparatus consists of an activated tantalum foil field ionizer interfaced with a 60 magnetic sector analyzer and a PDP 11123 computer for data acquisition and processing Approximateshyly 50 ~g of the sample is introduced via a heatable direct insertion probe Mass spectral data of the evolving volatiles are collected by repeatedly scanning the magnet over a preset range while the sample is gradually heated at a constant rate The sample holder is weighed before and after the analysis to determine the volatile fraction

The field ionization induces little fragmentashytion and so provides a determination of the pyrolyzates molecular weight The FIMS analyses were done using a programmed pyrolysis at -3degC min-J ofthe material into the inlet of the mass spectrometer (-270 Pa) The mass spectra were taken at regular intershyvals so that the evolution of individual comshypounds could be tracked as a function of temperature A sample of the SEYP lignin was run under similar conditions for comparishyson

The FIMS spectra (Fig 4) show that the two lignins yield similar products upon pyrolysis This feature had been observed in previous studies of eight other lignins9bullIO14 The main difference was in the evaporation curves (Fig 5) where the ALC lignin was found to volatilshyize at significantly lower temperatures which can be explained by the fact that it contains more low molecular mass products

The relative intensities of the peaks were also different between these two samples the main peak in ALC lignin is 332 amu while

III Measurement and modeling of lignin pyrolysis

Table 1 Elemental analysis of ALC lignin

Composition Round robin This (IJ) study

Carbon 669 674

Hydrogen 61 62

Oxygen 264 263

Nitrogen 02 02

Ash NR lt005

NR =~ot reported

Table 2 Possible monomeric structures in lignin tars

Molecular weight

Compound Molecular weight

Compound Molecular weight

Compound

94 Phenol 108 Cn=soI 110 Catechol

22 p-ethyl PbenolampXy1eshynols

124 Guaiacolamp Melbylcateshycool

126 Pyrogallol

138 Metbylguaiacol ampEtbyshyleatecbol

140 MdbosycateCbol 150 VinylguaiacoJ

152 Ethylpaiacolamp Vanillin

154 SyriDampoJ 164 Isoeugenol amp Eugenol

166 Acetovanilloneamp Propshyylguaiacol

168 Metbylsyringol ampVanshyniliitAcid

178 Coniferaldehyde

180 Propiovanillone ampViny-IsyringoJ

182 SyringaJdebyde ampEthyshylsyringol

194 Allylsyringol

1 Propylsytingolt ArtJD syringol

198 Syringic Add 208 Sinapaldehyde

210 Propiosyringone

Table 3 Possible dimeric structures in lignin tars

Molecular weight Compound

272 44-bis(hydroxy)33 -bis(methoxy)stilbene

332 44-bis(hydroxy)353-bis(mctboxy) stilbene

302 44-bis(hydroxy)33 -bis(methoxy)5-mcthoxy stilbene

418 Syringaresinol

in the SEYP lignin the 332 amu peak although present is not the highest peak which in that case is 418 amu Similar results were obtained by van der Rage et al who used temperature-resolved in-source PyMS and Curie-point pyrolysis GUMS to characterize the ALC and SEYP lignins26

Some of the prominent peaks present in the spectrum of both lignins are likely to correshyspond to coniferyl alcohol (MW 180)3shy

guaiacyl- I -propanol (MW = 182) sinapaldeshyhyde (MW = 208) and sinapyl alcohol (MW= 210) The probable compounds for each molshyecular weight are presented in Table 2 These assignments were made by Squire and Soloshymool4 based on the work of Klein and Allan and Mattila28 They are also consistent with recent work by van der Rage etal26 Some of the prominent peaks (MW = 272 amll MW= 302 amu MW 332 amu MW = 418

112 M A 8muo et aZ

710

i I

j lll~1 Jlll Illl l8Ct 80D IlOO nIO 80D 1JllO 1100

(JIIZ)

100 300 IlOO 700 900 1JllO 1100 Mass (MJZ)

Fig 4 Comparison of pyrolysis-FIMS summary spectra of (a) SEYP lignin and (b) ALC lignin The samples were heated at 3degC min-I into a vacuum chamber

amu) are thought to correspond to thermally stable dimers such as those indicated in Table 3 also from Ref [14)

Although the types of products are very similar between the two lignins the evolution rate of oligomers as a function of temperature shows slightly different patterns as discussed in Ref [14] for eight different Iignins and in Ref [26] for a set of five lignins including the ALe and SEYP These differences can be attributed primarily to the mode and severity of the lignin preparation method

Extraction

The two lignin samples were extracted with pyridine at the boiling point to obtain the amount and composition of the extract It was found that both the ALe and SEYP lignin are 100 soluble in pyridine This indicates that the network chains are relatively short This is confirmed by size exclusion chromatograshy

phy (SEC) results provided by Repap for the ALe lignin giving M-800 amu and M-l600-2000 amu with an overall range of 400-5000 amu21 The results reported from the lEA round robin analysis on acetylashyted SEYP were highly variable indicating M from 500 to 2000 AMU and Mow from 10000 to 4600020 However it is clear that the SEYP lignin has a higher average molecular weight than the ALe lignin

Summary of characterization methods

The elemental TG-FTIR pyrolysis-FIMS and solvent extraction analyses are used to describe the starting lignin and the major modes of decomposition The information from each test is used to calibra~e the pyrolysis model inputs The FIMS technique provides detailed insight into the tar formation processes The TG-FTIR analysis provides information on the gas product kinetics yields

_~~~~~r-~~-T~-n

4

I ~

shy

0 ____ _

-0

o~~~middotmiddot~~~~--~-r-r

Fig 5 Comparison of evaporation curves from pyrolysis-FIMS analysis of SEYP (-) and ALC ( - -) lignin

113 Measurement and modeling of lignin pyrolysis

StardDa Polymer

ilmiddotmiddotl~ ChR Ipal CIuIa

b During Tar Formation

c Char Formed

4OIJO Molecular Weight WIIlJ)

Tar ~ Char IpAI Char Fig 6 Schematic representation of coal or lignin polymer in the DVe simulation and corresponding

molecular weight distributions In this polymer the circles represent monomers (ring clusters and peripheral groups) The molecular weight shown by the numbers is the molecular weight of the monomer including the attached bridges The single line bridges are breakable and can donate

hydrogen The double line bridges are unbreakable and do not donate hydrogen The molecular weight distributions of the extractables tar and char are shown as a histogram at the right The area

under the histogram corresponds to the weight percent of the oligomers

and compositions the tar product kinetics yields and compositions and the char composhysition The solvent extraction work provides infonnation on the properties of the polymer network

MODEL DESCRIPTION

This description and the schematic represenshytation of the model in Fig 6 are based prishymarily on our work on coal The important distinctions in the case of modeling lignin have been noted in the text

Background on the statistical network model

The geometrical structure of a polymer (whether it is chain-like or highly crosslinked) controls how it degrades under otherwise identical chemical reactions One therefore requires statistical models based on the geoshymetrical structure to predict the degradation of a polymer At AFR we have developed such statistical models to describe the thermal decomposition of coal With the success of these concepts in describing coal it is logical to extend the macromolecular network conshycepts to describe lignin thennal decomposition behavior

The general model developed at AFR to describe coal thermal decomposition is the FG-DVC model I9 In developing this model

114 M A SERIO et oJ

extensive experimental work was done with synthetic polymers to allow the study of bond breaking and mass transport in a chemically clean systeml7middotIS The polymers used conshysisted of aromatic rings with different types of substituents and bridge structures The model combines two previously developed models a functional group (FG) rnode19

bull19

29 and a depolymerization-vaporization-crosslinking (DVC) rnodeI17

bull1S The DVe subroutine is

employed to determine the amount and molshyecular weight of macromolecular fragments The lightest of these fragments evolves as tar The FG subroutine is used to describe the gas evolution and the elemental and functional group compositions of the tar and char In the case of coal crosslinking in the DVe subroushytine is computed by assuming that this event is correlated with CO and CH evolutions predicted in the FG subroutine The dependshyence of the yield of rapidly released CO (which is related to coal rank or weathering) is the factor that controls the thermosetting or thermoplastic behavior of coals In the case of lignins which are generally thermoplastic it is assumed that there is no low-temperature crosslinking event The assumption was made that crosslinking is associated with the evolshyution of CH and ~O at high temperatures ie after the main tar formation peak

The Dve model

A simple example of the application of the Dve model to lignin (or coal) is shown in Fig 6 In this model the parent polymer is represented as a two-dimensional network of monomers (aromatic ring clusters) linked to form unbranched oligomers of length f by breakable (weak) and non-breakable (strong) bridges (shown as horizontal single or double lines respectively in Fig 6a) The monomers are represented by circles with molecular weights shown in each circle The molecular weight distribution of the monomers is assumed to be Gaussian and is described by two parameters M (mean) and 0 (standard deviation) mo crosslinks per gram are added (as vertical double lines in Fig 6a) to connect the oligomers of length Q so that the molecular weight between crosslinks Me matches the experimental value obtained from solvent swelling experiments If Me is not

available for a coal it is estimated or interposhylated based on measurements for related materials In the case of lignin Me is currentshyly considered to be an adjustable parameter The crosslinks form the branch points in the macromolecule Unconnected molecules (the extract yield) are obtained by choosing the value of Q A large value of Q will mean that a completely connected macromolecule will be formed when even a small number oirosslishynks are added leaving no extractable material For smaller values of Q some of the oligoshymers will be unattached after the crosslinks are added and these are the extractables The value of Q is determined automatically by matching the experimental extract yield

The parameters Me Q Mua and 0 determine the molecular weight distribution of oligomers in the starting molecule A histogram showshying the distribution created by randomly picking monomers to form oligomers of length bull and randomly crosslinking them to achieve an average molecular weight between crossshylinks Me is presented at the right of Fig 6a for the case of a bituminous coal where the initial pyridine extract yield is of order 25 wt The distribution is divided into a pyridine soluble portion below 3000 amu (light shading) and a pyridine insoluble porshytion above 3000 amu (dark shading) Since the amount of extractables is close to 100 for lignin this histogram is significantly changed for the starting lignin polymer

Figure 6b shows the polymer during pyrolysis The rates for bridge breaking and crosslinking are determined from the FG model Some bridges have broken other bridges have been converted to unbreakable bridges by the abstraction of hydro~en to stabilize the free radicals and new crosslinks have been formed To determine the change of state of the computer molecules during a time step the number of crosslinks formed is determined using the FG subroutine and passed to the Dve subroutine These crossshylinks are distributed randomly throughout the char assuming that the crosslinking probabilshyity is proportional to the molecular weight of the monomer Then the Dve subroutine breaks the appropriate number of bridging bonds and calculates the quantity of tar evolved for this time step using the transport equations The result is the hypothetical coal

115 Measurement and modeling of lignin pyrolysis

INPUT DATA

Functional Group Pools Monomer bullbullCrosslinking Events ~Bridges

Crosslinks

Breakable Bonds

- - -t- - - y 1- High Heating I I Bate Predictions--------

MODEL

PREDICTIONS

Fig 7 Structure of FG-DVC model and major inputs and outputs

or lignin molecule representation and the molecular weight distributions shown in Fig 6b The lighter tar molecules which leave the particle according to the transport equashytions are cross hatched in Fig 6b In the case ofcoal a fraction of the donatable hydrogen is used to stabilize the free radicals formed by bridge breaking creating two new methyl groups per bridge and the same fraction of breakable bridges is converted into unbreakshyable bridges which are represented in the model as double bonds As discussed below this last feature was eliminated in the lignin version of the model because of the larger amount of donatable hydrogen In addition the type of bridges present in lignin are not assumed to be ethylene bridges although the calculations do not depend on this assumption since the activiation energy distribution for the bridge breaking process is determined empirishycally

Figure 6c shows the final char which is highly crosslinked with unbreakable bridges and has no remaining donatable hydrogen in the case of coal In the case of lignin there is no mechanism for creating unbreakable bridges via hydrogen abstraction so the char structure is held together by the initial crossshylinks and the cross links that form during pyrolysis In either case the histogram now shows only tar and pyridine insoluble fracshytions The extractables have been eliminated

by tar formation and crosslinking The output of the Dve subroutine is the molecular weight distribution in the polymer its timeshydependent transformation during devolatilizshyation and the evolution of tar determined by the transport of the lighter components

The FG model

The Functional Group (FG) model has been described previously91929 It permits the detailed prediction of the composition of volatile species (gas yield tar yield and tar functional group and elemental composition) and of char (elemental and functional group composition) The original version employed coal- or lignin-independent rates for the deshycomposition of individual functional groups to produce gas species The ultimate y1elds of each gas species are related to the functional group composition This approach was successful in predicting the evolution profiles and amounts of the volatile products for

l4coal l929 and lignin9 Recently it was found

that rank-dependent kinetic rates for bridge breaking and cross linking were required to make accurate predictions of coal fluidity data as discussed in Ref [30] In general the 10 lignins that have been studied in our labshyoratory so far are much less variable in their pyrolysis behavior than a lignite compared to a low-volatile bituminous coal for example

114 M A SERIO et oJ

extensive experimental work was done with synthetic polymers to allow the study of bond breaking and mass transport in a chemically clean systeml7middotIS The polymers used conshysisted of aromatic rings with different types of substituents and bridge structures The model combines two previously developed models a functional group (FG) rnode19

bull19

29 and a depolymerization-vaporization-crosslinking (DVC) rnodeI17

bull1S The DVe subroutine is

employed to determine the amount and molshyecular weight of macromolecular fragments The lightest of these fragments evolves as tar The FG subroutine is used to describe the gas evolution and the elemental and functional group compositions of the tar and char In the case of coal crosslinking in the DVe subroushytine is computed by assuming that this event is correlated with CO and CH evolutions predicted in the FG subroutine The dependshyence of the yield of rapidly released CO (which is related to coal rank or weathering) is the factor that controls the thermosetting or thermoplastic behavior of coals In the case of lignins which are generally thermoplastic it is assumed that there is no low-temperature crosslinking event The assumption was made that crosslinking is associated with the evolshyution of CH and ~O at high temperatures ie after the main tar formation peak

The Dve model

A simple example of the application of the Dve model to lignin (or coal) is shown in Fig 6 In this model the parent polymer is represented as a two-dimensional network of monomers (aromatic ring clusters) linked to form unbranched oligomers of length f by breakable (weak) and non-breakable (strong) bridges (shown as horizontal single or double lines respectively in Fig 6a) The monomers are represented by circles with molecular weights shown in each circle The molecular weight distribution of the monomers is assumed to be Gaussian and is described by two parameters M (mean) and 0 (standard deviation) mo crosslinks per gram are added (as vertical double lines in Fig 6a) to connect the oligomers of length Q so that the molecular weight between crosslinks Me matches the experimental value obtained from solvent swelling experiments If Me is not

available for a coal it is estimated or interposhylated based on measurements for related materials In the case of lignin Me is currentshyly considered to be an adjustable parameter The crosslinks form the branch points in the macromolecule Unconnected molecules (the extract yield) are obtained by choosing the value of Q A large value of Q will mean that a completely connected macromolecule will be formed when even a small number oirosslishynks are added leaving no extractable material For smaller values of Q some of the oligoshymers will be unattached after the crosslinks are added and these are the extractables The value of Q is determined automatically by matching the experimental extract yield

The parameters Me Q Mua and 0 determine the molecular weight distribution of oligomers in the starting molecule A histogram showshying the distribution created by randomly picking monomers to form oligomers of length bull and randomly crosslinking them to achieve an average molecular weight between crossshylinks Me is presented at the right of Fig 6a for the case of a bituminous coal where the initial pyridine extract yield is of order 25 wt The distribution is divided into a pyridine soluble portion below 3000 amu (light shading) and a pyridine insoluble porshytion above 3000 amu (dark shading) Since the amount of extractables is close to 100 for lignin this histogram is significantly changed for the starting lignin polymer

Figure 6b shows the polymer during pyrolysis The rates for bridge breaking and crosslinking are determined from the FG model Some bridges have broken other bridges have been converted to unbreakable bridges by the abstraction of hydro~en to stabilize the free radicals and new crosslinks have been formed To determine the change of state of the computer molecules during a time step the number of crosslinks formed is determined using the FG subroutine and passed to the Dve subroutine These crossshylinks are distributed randomly throughout the char assuming that the crosslinking probabilshyity is proportional to the molecular weight of the monomer Then the Dve subroutine breaks the appropriate number of bridging bonds and calculates the quantity of tar evolved for this time step using the transport equations The result is the hypothetical coal

115 Measurement and modeling of lignin pyrolysis

INPUT DATA

Functional Group Pools Monomer bullbullCrosslinking Events ~Bridges

Crosslinks

Breakable Bonds

- - -t- - - y 1- High Heating I I Bate Predictions--------

MODEL

PREDICTIONS

Fig 7 Structure of FG-DVC model and major inputs and outputs

or lignin molecule representation and the molecular weight distributions shown in Fig 6b The lighter tar molecules which leave the particle according to the transport equashytions are cross hatched in Fig 6b In the case ofcoal a fraction of the donatable hydrogen is used to stabilize the free radicals formed by bridge breaking creating two new methyl groups per bridge and the same fraction of breakable bridges is converted into unbreakshyable bridges which are represented in the model as double bonds As discussed below this last feature was eliminated in the lignin version of the model because of the larger amount of donatable hydrogen In addition the type of bridges present in lignin are not assumed to be ethylene bridges although the calculations do not depend on this assumption since the activiation energy distribution for the bridge breaking process is determined empirishycally

Figure 6c shows the final char which is highly crosslinked with unbreakable bridges and has no remaining donatable hydrogen in the case of coal In the case of lignin there is no mechanism for creating unbreakable bridges via hydrogen abstraction so the char structure is held together by the initial crossshylinks and the cross links that form during pyrolysis In either case the histogram now shows only tar and pyridine insoluble fracshytions The extractables have been eliminated

by tar formation and crosslinking The output of the Dve subroutine is the molecular weight distribution in the polymer its timeshydependent transformation during devolatilizshyation and the evolution of tar determined by the transport of the lighter components

The FG model

The Functional Group (FG) model has been described previously91929 It permits the detailed prediction of the composition of volatile species (gas yield tar yield and tar functional group and elemental composition) and of char (elemental and functional group composition) The original version employed coal- or lignin-independent rates for the deshycomposition of individual functional groups to produce gas species The ultimate y1elds of each gas species are related to the functional group composition This approach was successful in predicting the evolution profiles and amounts of the volatile products for

l4coal l929 and lignin9 Recently it was found

that rank-dependent kinetic rates for bridge breaking and cross linking were required to make accurate predictions of coal fluidity data as discussed in Ref [30] In general the 10 lignins that have been studied in our labshyoratory so far are much less variable in their pyrolysis behavior than a lignite compared to a low-volatile bituminous coal for example

115 Measurement and modeling of lignin pyrolysis

INPUT DATA

Functional Group Pools Monomer bullbullCrosslinking Events ~Bridges

Crosslinks

Breakable Bonds

- - -t- - - y 1- High Heating I I Bate Predictions--------

MODEL

PREDICTIONS

Fig 7 Structure of FG-DVC model and major inputs and outputs

or lignin molecule representation and the molecular weight distributions shown in Fig 6b The lighter tar molecules which leave the particle according to the transport equashytions are cross hatched in Fig 6b In the case ofcoal a fraction of the donatable hydrogen is used to stabilize the free radicals formed by bridge breaking creating two new methyl groups per bridge and the same fraction of breakable bridges is converted into unbreakshyable bridges which are represented in the model as double bonds As discussed below this last feature was eliminated in the lignin version of the model because of the larger amount of donatable hydrogen In addition the type of bridges present in lignin are not assumed to be ethylene bridges although the calculations do not depend on this assumption since the activiation energy distribution for the bridge breaking process is determined empirishycally

Figure 6c shows the final char which is highly crosslinked with unbreakable bridges and has no remaining donatable hydrogen in the case of coal In the case of lignin there is no mechanism for creating unbreakable bridges via hydrogen abstraction so the char structure is held together by the initial crossshylinks and the cross links that form during pyrolysis In either case the histogram now shows only tar and pyridine insoluble fracshytions The extractables have been eliminated

by tar formation and crosslinking The output of the Dve subroutine is the molecular weight distribution in the polymer its timeshydependent transformation during devolatilizshyation and the evolution of tar determined by the transport of the lighter components

The FG model

The Functional Group (FG) model has been described previously91929 It permits the detailed prediction of the composition of volatile species (gas yield tar yield and tar functional group and elemental composition) and of char (elemental and functional group composition) The original version employed coal- or lignin-independent rates for the deshycomposition of individual functional groups to produce gas species The ultimate y1elds of each gas species are related to the functional group composition This approach was successful in predicting the evolution profiles and amounts of the volatile products for

l4coal l929 and lignin9 Recently it was found

that rank-dependent kinetic rates for bridge breaking and cross linking were required to make accurate predictions of coal fluidity data as discussed in Ref [30] In general the 10 lignins that have been studied in our labshyoratory so far are much less variable in their pyrolysis behavior than a lignite compared to a low-volatile bituminous coal for example

116 M A SERJOet al

Consequently it is unlikely that Jignin-dependshyent kinetic parameters will be required although the amounts of each functional group and the distribution of bridge types will cershytainly be variable for each lignin

MODEL IMPLEMENTATION FOR LIGNIN

The implementation of the FG-DVC model for a complex polymer like lignin (or coal) requires the specification of several parameshyters some of which can be constrained by the known structural units and some of which are constrained by experimental characterization data A flow diagram of the model inputs and outputs is given in Fig 7 The basic idea is to calibrate the model using simple small scale experiments like TG-FITR FIMS and elemshyental analysis and then use the model to make predictions for conditions where experimental data are not readily available This approach has been highly successful in using the FGshyDVC model to make predictions for pyrolysis at very high heating rates using a model that was validated using low-heating-rate TGshyFTIR pyrolysis-FIMS and fluidity data 19

bulllO

In the case of lignin the high heating rate data was not available for the ALC lignin so this paper is concerned with the calibration phase of the modeling effort

Network

First the polymer network had be to defined In the case of lignin little is known on the exact structure of the polymer Howshyever from the nature of the products evolved one can reasonably assume that the network is composed of several types of monomers among those listed in Tables 2 and 3 linked by labile or unbreakable bridges Since many different types of bridges can be present in lignin the exact nature (ie mass etc) of the bridges in the model was not specified An activation energy distribution is used for the bridge breaking reaction in order to account for these variations The monomers were considered as black boxes linked together by bridges (either labile or unbreakable) of neglishygible mass It is reasonable to assume that the lignin network is three-dimensional To represent such a structure cross links were included in the network which act as unbreakshy

able branch points The unbreakable bridges (or hard bonds) also used in the model differ from crosslinks in the sense that they are part of a linear chain The monomer types and distribution were chosen in order to fit the main peaks of the pyrolysis-FIMS data

Depolymerization reactions

It has been shown that the primary types of labile bridges in lignin are of the CoO or C-C type The depolymerization process done by breaking labile bridges can be performed with hydrogen abstraction from either other labile bridges if those can give hydrogen (like in our original coal depolymerization model) or other possible hydrogen donor species in the polymer In the case of lignin as indicated by examples of possible monomers in Table 2 part of the structure consists of short aliphatic chains (-CH2~-) which can easily provide hydrogen As a result it was assumed in the model that hydrogen donation was not a limiting step in the process of depolymerization ie that every broken bridge was automatically provided with sufficient hydrogen A second variation on the coal depolymerization mechanism is that the hydroshygen donation process does not result in the creation of unbreakable bonds in the bridge or side group which is donating the hydrogen

Crosslinking reactions

In the case of lignin an important reaction to model is the crosslinking of the polymer In the version of the FG-DVC model used for coal crosslinking reactions are related to gas evolution in particular CO and CH26 It seems obvious that in the case of lignin crosslinking events could also be related to gas evolution although not necessarily the same gases

A condensation reaction producing an ether bond and evolving water or methanol is thought to occur in lignin as shown in Fig 814 This reaction starts as a result of depolymerization reactions when phenoxy radicals are formed after homolytic cleavage ofa and pethers and continues as the aroshymatic methoxyl groups cleave into additional methyl (which after abstracting hydrogen evolve as methane) and phenoxy radicals

117 Measurement and modeling of lignin pyrolysis

Fig 8 Proposed crosslinking reactions in lignin

The -o-q bond is stable under pyrolysis conditions and can be considered in our model as a crosslink Water is also thought to form at low temperature by elimination from aliphatic hydroxyls forming C-G-C bridges These bridges are not stable under pyrolysis conditions ie cannot be considered as crossshylinks The low-temperature peak in the water evolution corresponds to dehydration from aliphatic hydroxyls while the higher temperashyture peak is attributed to the condensation reaction producing diaryl ethers ~ (see Fig2d)

Based on these considerations crosslinking in the model was associated with the high temperature water evolution as well as with the methane evolution since the reaction shown in Fig 8 occurs after aromatic methshyoxyl groups cleave to form methyl It could also be correlated with methanol evolution since this will result from the addition of the phenoxy radical to a methoxy group also shown in Fig 8 However since the highshytemperature water and the methanol evolve in the same temperature range for the ALe lignin as would be expected if the mechanism in Fig 8 is correct only one of these gases needs to be correlated with a cross linking event

Efficiencies of 1 (Le one crosslink per gas molecule evolved) were used for water and methane and the crosslink sites were chosen

to be as in the coal version of the model on aromatic rings In future versions of the model both water and methanol will be correshylated with crosslinking events with approxishymately reduced cross linking efficiencies

Vaporization

In the original FG-DVC model tars are transported out of the particle with the light devolatilization products that exit the coal via bubbles or pores The rate of transport for tar components is then proportional to the volume of gas evolved dnJdt = Pj X L (dnldt)P5 1(P+AP) ilt300

(1)

where dnJdt = rate of vaporization of oligoshymers of mass j Pi = vapor pres~re of oligomer of mass j Xf bulk mole fraction of oligomers of mass jI(dnJdt)gas = sum of vaporization rate of products of molecular weight less than 300 amu P =ambient pressure and AP pressure difference between the surface and the particles interior

In the case of lignin however since the network is weakly crosslinked and chains are relatively short diffusion can also be an important mechanism of tar escape In that case the transport equation becomes

118 M A SERIO et aL

with K == a constant to be determined using FIMS and TG-FTIR data

Since it is not known which transport mechshyanism (tar escape through pores case A or by diffusion case B) dominates simulations were performed for both cases It is also assumed for simplicity that the pressure inside the particle is the same as the ambient pressure ie M O This is a realistic assumption for a polymer which melts like most lignins

Gas evolution

For aromatic polymers the gas evolution occurs from decomposition of peripheral groups including bridge structures In the FGshyDVe model these groups are distributed based on the known polymer composition using a mixture of functional group sources This is the FG part of the model The specific mechanisms of gas evolution have not been input into the model with the same level of detail as the char and tar formation mechshyanisms (treated in the DVe part of the model) It has been found that the absence of detailed gas formation mechanisms has not prevented us from accurately predicting gas formation from coal over a wide range of heating rates (oosee S-I to 20OOOee S-I)1930

Since lignin has a structure relatively close to low-rank coals and produces the same gases the same functional groups as in the case of coal were used91415 Future versions of the model will also include gases such as CH30H and CHzO which are important prodshyucts for lignin but not for coal

Experimental inputs

The next step in setting up the model is to use experimental data such as elemental analysis data to further constrain the model This is shown schematically in Fig 7 The amount of each functional group is conshystrained by matching the TG-FTIR gas evolshyution data and the elemental analysis A second experimental input required for polyshymers which have an indefinite structure such as lignin is the number of starting crosslinks This can be based on either the volumetric swelling ratio or the pyridine extractables The latter quantity was used in the current

simulations

SIMULATIONS OF ALe L1GSIN PYROLYSIS

Simulations of ALe lignin pyrolysis were performed for comparison with pyrolysis results obtained for TG-FTIR analysis and pyrolysis-FIMS analysis These comparisons are used to further refine the polymer composhysition andor kinetic files in an iterative fashion as shown in Fig 7 The calibrated model can then be used to make predictions with reasonable confidence for conditions where experimental data are not available such as for high heating rates

The monomer types and distributions were chosen to fit the pyrolysis-FIMS data Table 4 presents the monomers used in the simulashytions as well as their amounts Although they could be considered as dimers (ie combinashytion of monomers) the compounds ofmolecushylar weights 272302 332 and 418 amll were taken to be as monomers The main reason is the fact that these are extremely stable dimers and are found in large amounts as shown in the pyrolysis-FIMS results (see Fig 4) It was also found to be necessary to use a double distribution of monomers in order to represent the unattached oligomers as sugshygested by the early evolution of some species in the pyrolysis-FIMS data (discussed above) The second distribution is then only relevant to those free oligomers

The network parameters (amount of availshyable hydrogen initial crosslink density and starting oligomer length) were chosen to match the experimental value of pyridine extractables (100) the amount of tar from TG-FfIR experiments (-50) and the values of the molecular weight distribution Mn-800 amu Mw-l600-2000 amu with an overall range of 400-5000 amu provided by the Repap Technologies Inc In the model the actual value of pyridine extractables (which is approximated by the sum of masses less than 3000 amu) has been slightly modified from 100 to 85 in order to take into account the overall range of molecular weights which goes up to 5000 amu These parameters are summarized in Table 5 For both case A and case B it appears that the number of labile bridges per monomer is somewhat lower and

117 Measurement and modeling of lignin pyrolysis

Fig 8 Proposed crosslinking reactions in lignin

The -o-q bond is stable under pyrolysis conditions and can be considered in our model as a crosslink Water is also thought to form at low temperature by elimination from aliphatic hydroxyls forming C-G-C bridges These bridges are not stable under pyrolysis conditions ie cannot be considered as crossshylinks The low-temperature peak in the water evolution corresponds to dehydration from aliphatic hydroxyls while the higher temperashyture peak is attributed to the condensation reaction producing diaryl ethers ~ (see Fig2d)

Based on these considerations crosslinking in the model was associated with the high temperature water evolution as well as with the methane evolution since the reaction shown in Fig 8 occurs after aromatic methshyoxyl groups cleave to form methyl It could also be correlated with methanol evolution since this will result from the addition of the phenoxy radical to a methoxy group also shown in Fig 8 However since the highshytemperature water and the methanol evolve in the same temperature range for the ALe lignin as would be expected if the mechanism in Fig 8 is correct only one of these gases needs to be correlated with a cross linking event

Efficiencies of 1 (Le one crosslink per gas molecule evolved) were used for water and methane and the crosslink sites were chosen

to be as in the coal version of the model on aromatic rings In future versions of the model both water and methanol will be correshylated with crosslinking events with approxishymately reduced cross linking efficiencies

Vaporization

In the original FG-DVC model tars are transported out of the particle with the light devolatilization products that exit the coal via bubbles or pores The rate of transport for tar components is then proportional to the volume of gas evolved dnJdt = Pj X L (dnldt)P5 1(P+AP) ilt300

(1)

where dnJdt = rate of vaporization of oligoshymers of mass j Pi = vapor pres~re of oligomer of mass j Xf bulk mole fraction of oligomers of mass jI(dnJdt)gas = sum of vaporization rate of products of molecular weight less than 300 amu P =ambient pressure and AP pressure difference between the surface and the particles interior

In the case of lignin however since the network is weakly crosslinked and chains are relatively short diffusion can also be an important mechanism of tar escape In that case the transport equation becomes

118 M A SERIO et aL

with K == a constant to be determined using FIMS and TG-FTIR data

Since it is not known which transport mechshyanism (tar escape through pores case A or by diffusion case B) dominates simulations were performed for both cases It is also assumed for simplicity that the pressure inside the particle is the same as the ambient pressure ie M O This is a realistic assumption for a polymer which melts like most lignins

Gas evolution

For aromatic polymers the gas evolution occurs from decomposition of peripheral groups including bridge structures In the FGshyDVe model these groups are distributed based on the known polymer composition using a mixture of functional group sources This is the FG part of the model The specific mechanisms of gas evolution have not been input into the model with the same level of detail as the char and tar formation mechshyanisms (treated in the DVe part of the model) It has been found that the absence of detailed gas formation mechanisms has not prevented us from accurately predicting gas formation from coal over a wide range of heating rates (oosee S-I to 20OOOee S-I)1930

Since lignin has a structure relatively close to low-rank coals and produces the same gases the same functional groups as in the case of coal were used91415 Future versions of the model will also include gases such as CH30H and CHzO which are important prodshyucts for lignin but not for coal

Experimental inputs

The next step in setting up the model is to use experimental data such as elemental analysis data to further constrain the model This is shown schematically in Fig 7 The amount of each functional group is conshystrained by matching the TG-FTIR gas evolshyution data and the elemental analysis A second experimental input required for polyshymers which have an indefinite structure such as lignin is the number of starting crosslinks This can be based on either the volumetric swelling ratio or the pyridine extractables The latter quantity was used in the current

simulations

SIMULATIONS OF ALe L1GSIN PYROLYSIS

Simulations of ALe lignin pyrolysis were performed for comparison with pyrolysis results obtained for TG-FTIR analysis and pyrolysis-FIMS analysis These comparisons are used to further refine the polymer composhysition andor kinetic files in an iterative fashion as shown in Fig 7 The calibrated model can then be used to make predictions with reasonable confidence for conditions where experimental data are not available such as for high heating rates

The monomer types and distributions were chosen to fit the pyrolysis-FIMS data Table 4 presents the monomers used in the simulashytions as well as their amounts Although they could be considered as dimers (ie combinashytion of monomers) the compounds ofmolecushylar weights 272302 332 and 418 amll were taken to be as monomers The main reason is the fact that these are extremely stable dimers and are found in large amounts as shown in the pyrolysis-FIMS results (see Fig 4) It was also found to be necessary to use a double distribution of monomers in order to represent the unattached oligomers as sugshygested by the early evolution of some species in the pyrolysis-FIMS data (discussed above) The second distribution is then only relevant to those free oligomers

The network parameters (amount of availshyable hydrogen initial crosslink density and starting oligomer length) were chosen to match the experimental value of pyridine extractables (100) the amount of tar from TG-FfIR experiments (-50) and the values of the molecular weight distribution Mn-800 amu Mw-l600-2000 amu with an overall range of 400-5000 amu provided by the Repap Technologies Inc In the model the actual value of pyridine extractables (which is approximated by the sum of masses less than 3000 amu) has been slightly modified from 100 to 85 in order to take into account the overall range of molecular weights which goes up to 5000 amu These parameters are summarized in Table 5 For both case A and case B it appears that the number of labile bridges per monomer is somewhat lower and

118 M A SERIO et aL

with K == a constant to be determined using FIMS and TG-FTIR data

Since it is not known which transport mechshyanism (tar escape through pores case A or by diffusion case B) dominates simulations were performed for both cases It is also assumed for simplicity that the pressure inside the particle is the same as the ambient pressure ie M O This is a realistic assumption for a polymer which melts like most lignins

Gas evolution

For aromatic polymers the gas evolution occurs from decomposition of peripheral groups including bridge structures In the FGshyDVe model these groups are distributed based on the known polymer composition using a mixture of functional group sources This is the FG part of the model The specific mechanisms of gas evolution have not been input into the model with the same level of detail as the char and tar formation mechshyanisms (treated in the DVe part of the model) It has been found that the absence of detailed gas formation mechanisms has not prevented us from accurately predicting gas formation from coal over a wide range of heating rates (oosee S-I to 20OOOee S-I)1930

Since lignin has a structure relatively close to low-rank coals and produces the same gases the same functional groups as in the case of coal were used91415 Future versions of the model will also include gases such as CH30H and CHzO which are important prodshyucts for lignin but not for coal

Experimental inputs

The next step in setting up the model is to use experimental data such as elemental analysis data to further constrain the model This is shown schematically in Fig 7 The amount of each functional group is conshystrained by matching the TG-FTIR gas evolshyution data and the elemental analysis A second experimental input required for polyshymers which have an indefinite structure such as lignin is the number of starting crosslinks This can be based on either the volumetric swelling ratio or the pyridine extractables The latter quantity was used in the current

simulations

SIMULATIONS OF ALe L1GSIN PYROLYSIS

Simulations of ALe lignin pyrolysis were performed for comparison with pyrolysis results obtained for TG-FTIR analysis and pyrolysis-FIMS analysis These comparisons are used to further refine the polymer composhysition andor kinetic files in an iterative fashion as shown in Fig 7 The calibrated model can then be used to make predictions with reasonable confidence for conditions where experimental data are not available such as for high heating rates

The monomer types and distributions were chosen to fit the pyrolysis-FIMS data Table 4 presents the monomers used in the simulashytions as well as their amounts Although they could be considered as dimers (ie combinashytion of monomers) the compounds ofmolecushylar weights 272302 332 and 418 amll were taken to be as monomers The main reason is the fact that these are extremely stable dimers and are found in large amounts as shown in the pyrolysis-FIMS results (see Fig 4) It was also found to be necessary to use a double distribution of monomers in order to represent the unattached oligomers as sugshygested by the early evolution of some species in the pyrolysis-FIMS data (discussed above) The second distribution is then only relevant to those free oligomers

The network parameters (amount of availshyable hydrogen initial crosslink density and starting oligomer length) were chosen to match the experimental value of pyridine extractables (100) the amount of tar from TG-FfIR experiments (-50) and the values of the molecular weight distribution Mn-800 amu Mw-l600-2000 amu with an overall range of 400-5000 amu provided by the Repap Technologies Inc In the model the actual value of pyridine extractables (which is approximated by the sum of masses less than 3000 amu) has been slightly modified from 100 to 85 in order to take into account the overall range of molecular weights which goes up to 5000 amu These parameters are summarized in Table 5 For both case A and case B it appears that the number of labile bridges per monomer is somewhat lower and

119 Measurement and modeling of lignin pyrolysis

Table 4 Monomer distribution used in ALC lignin pyrolysis

Molecular weight

Mole fraction

Case A CaseB

Regular network

Guest monomers

Regular network

Guest mono-men

138 0042

152 0049

154 0056

166 0063

168 0063

180 0056

182 0056

194 0049

196 0049

208 0049

210 0042

272 0035 0035

302 0Q35 0056

332 0028 0141

418 0028 0063

0046

0053

0061

0069

0069

0061

0061

0053

0053

0053

0046 0030

0038 0Q38

0038 0130

0030 0038

the number of crosslinks per monomer is somewhat higher than the common lignin models would indicate In order to determine if these are reasonable it will be necessary to examine the composition of lignin just prior to the main depolymerization reaction (tar formashytion) since this may be different from the initial structure It is also true that incorrect network parameters could compensate for other deficiencies in the model This aspect of the model will require further study

The bridge breaking rate was adjusted to fit both the TG-FTIR and pyrolysis-FIMS tar evolution data It was shown from the pyrolysis-FIMS analysis that low molecular weight compounds only evolve after extensive bridge breaking occurs Since these comshy

pounds have low molecular weights their evolution is not limited by vaporization but only by bridge breaking Their evolution temperature is then directly dependent on the bridge breaking rate While the FIMS data provided an idea of the magnitude of the bridge breaking rate at 4dege min-I the comshyplete kinetics for tar and gases were obtained using the TG-FTIR data The kinetic parameshyters were selected by simultaneously fitting evolution curves for different heating rates

Case A tar escape through pores

The transport equation (I) from the original FG-DVe model was used as described middotabove The parameters used are shown in Table 5 It

Table 5 Summary of parame1ers used in ALC lignin pyrolysis simulations

Hard borxls per Initial crosslinks per Labile bridges per monomer monomer monomer

Case A 004 027 063

Case B 017 025 050

120

~ 12311( ampp II231C

l1i

~~ 103

02 sa 01

00

Sim562 ampp 7I5IC

M A SERIO et aL

_03

lu COz Sill 2OIJI ampp 304

1

_10 c Ii

02 1middot 1u 1deg1 gt

gt=18 0

0

00

i eM Sill 17 ampp Lfn f_u004

102 102i 01 10~ 01 b

G00 B00 0 10 90100

50 188000 90

i900Tar Si 1$ eo800 700

Exgt 387f40 7G

CJ eoGOO- 30 ~ 60i 600 ~

40t2 20 400 3l 30f 300 ~o 20 11

200 10

I- 00 I- 100

00 10 20 30 40 60 60 70 60 110 1laquoJdeg Pyrolysis 11m (min)

o 1

Fig 9 Comparison of FG-DVC model predictions (solid lines) with pyrolysis data (circles) at lOoe

1000 100E 140 900 80Tar S1m9 eo iamp00Exp4a73lu 70700100 (l

o0 4 bull 12 11 20 24 2amp 32 3 bull 40deg

f800 eoIl 80 lt SIlO ISO1 0 41 bull 40

1 3040

20C20 1laquoJ 10~ 00

0 4 Pyrolysis 11m (min)

Fig 10 Compuison of FG-DVC model predictions (solid lines) with pyrolysis data (circles) at 30C min-I for ALC lignin Case A (a-e) Gases and tars (f) time-temperature profile and weight loss

miD-I for ALe lignin Case A (a-e) Gases and tars (f) time-tem~e profile and

08_ c sn 1

18~ COz Exp 171 14

-12tQ4

10103 10ampI 02 G6

gt= gt=0-4 ~ 01 a ~ 02

00 004

10

d

0

Sim 24114 Exp 2l5IIC

CH4f iu08

06 06 1 04 a 04 ~ 03

02 J02 ~ 01

00 8 00

Simll Exp1Uft

~

CO Sim 54114 Up UII(

weight loss

120

~ 12311( ampp II231C

l1i

~~ 103

02 sa 01

00

Sim562 ampp 7I5IC

M A SERIO et aL

_03

lu COz Sill 2OIJI ampp 304

1

_10 c Ii

02 1middot 1u 1deg1 gt

gt=18 0

0

00

i eM Sill 17 ampp Lfn f_u004

102 102i 01 10~ 01 b

G00 B00 0 10 90100

50 188000 90

i900Tar Si 1$ eo800 700

Exgt 387f40 7G

CJ eoGOO- 30 ~ 60i 600 ~

40t2 20 400 3l 30f 300 ~o 20 11

200 10

I- 00 I- 100

00 10 20 30 40 60 60 70 60 110 1laquoJdeg Pyrolysis 11m (min)

o 1

Fig 9 Comparison of FG-DVC model predictions (solid lines) with pyrolysis data (circles) at lOoe

1000 100E 140 900 80Tar S1m9 eo iamp00Exp4a73lu 70700100 (l

o0 4 bull 12 11 20 24 2amp 32 3 bull 40deg

f800 eoIl 80 lt SIlO ISO1 0 41 bull 40

1 3040

20C20 1laquoJ 10~ 00

0 4 Pyrolysis 11m (min)

Fig 10 Compuison of FG-DVC model predictions (solid lines) with pyrolysis data (circles) at 30C min-I for ALC lignin Case A (a-e) Gases and tars (f) time-temperature profile and weight loss

miD-I for ALe lignin Case A (a-e) Gases and tars (f) time-tem~e profile and

08_ c sn 1

18~ COz Exp 171 14

-12tQ4

10103 10ampI 02 G6

gt= gt=0-4 ~ 01 a ~ 02

00 004

10

d

0

Sim 24114 Exp 2l5IIC

CH4f iu08

06 06 1 04 a 04 ~ 03

02 J02 ~ 01

00 8 00

Simll Exp1Uft

~

CO Sim 54114 Up UII(

weight loss

121 Measurement and modeling of lignin pyrolysis

20

i 14 12

fA 110

otI a 04

802 000

01-0 c 18 1l18 114 J 12ampo i ot

011gt04 02 0 00

400

f 3amp0

132 0 aubull 240J 200 160

j 120 ) 80 iii 40 I- 00

~

COz

a

CH4

b

Tar

C

3 8

SIal 1951 fIp 242

11m UK IIp 2

8 PytOIySUI Time (min)

tO Sill 11274t ~tO iIp1927I 7

I eo LII

I 40 lIO

i u f 00

$Ion 51 IIp U2I~CQ

i UI r 1A1 ~ gt~te

15 8 00 Om

1000 toO f

t -8CO 700

(J - 1500

I 300 200

10 100

1215 Co J 6 15deg Pyrolysi$ Time (min)

Fig II Comparison of FG-DVC model predictions (solid lines) with pyrolysis data (circles) at 100degC min-I for ALC lignin Case A (a-e) Gases and tars (I) time-temperature profile and weight loss

10

80

60

40

20

0

a

0 SOO 1000 100

b 80

c 80

60

40

2+-+Ifiillllll~---lt1~I+-JIIlIIII+--+-t-I

o 500 1000 Molecular Weight

Fig 12 Comparison of measured and predicted pyrolysis-FIMS summary spectra for ALC lignin (a) Data (b) case A and (c) case B

JB71+-1

122 M A SERIO et al

dego~~~~~~~~ 100~~o-~~~~~~~

i 80

Fig 13 Comrruison of measured and predicted temperature resolved pyrolysis-FIMS tar evorution for ALC lignin (a) Data (b) case A and (c) case B

was possible to simulate reasonably well the TG-FIIR data (see Figs 9-1 1) for the three different heating rates Note that the tar amount increases with heating rate and that the model was able to predict this trend The predicted oligomer distribution also compares well with summary plot of the pyrolysis-FIMS data (Fig 12b) However the temperatures at which products evolve happen to be higher than those the temperatureshyresolved pyrolysis-FIMS data would indicate as shown in Fig 13b

The tar evolution was found to correspond to a bridge breaking mean activation energy of 49 kcal mol-I which is somewhat smaller than the mean activation energy found in the case of low rank coals of 52 kcal mol-1

bull30 This is

consistent with the fact that the tar peak evolution rate for lignin occurs at lower temshyperatures (-400degC vs 425degC at 30degC min-I) In both cases a preexponential factor of 1014

S-I is assumed

Case B tar escape through diffusion

In this case the modified transport equation (2) was used as described above along with the second set of network parameters from Table 5 The proportionality constant K in the vaporization law was adjusted so that the unattached oligomers evolve at the temperashyture found during the pyrolysis-FIMSexperishyment By doing so with one type of unatshytached monomer (MW = 332 amu) it was possible to predict the evolution temperature of another unattached monomer (MW = 418 amu) which indicates that the molecular weight dependence in the vaporization law is relatively accurate The predicted results compared well with the pyrolysis-FIMS data (Figs 12c and 13c) concerning both the final oligomer distribution and evolution temperashytures The features of the TG-FIIR data were fairly well represented except for the increase in the tar yield with heating rate

123 Measurement and modling of lignin pyrolysis

The mean activation energy for bridge breaking which gave the best results for case B is only slightly different from that in the preceding case (50 kcaJ instead of 49 kcaJ) The difference is simply due to the fact that the vaporization process is a convolution of the bridge breaking and transport processes so that changing one will require changing the other in order to simulate a given data set

In conclusion both Cases A and B present relatively good simulations of the available data The limitations observed (oligomers evolution temperature too high for Case A and lack of increase in tar with heating rate for Case B) should be reduced with a better treatment of tar transport mechanisms

CONCLUSIONS

The FG-DVC model demonstrated its ability to represent the thermal decomposition of an organosolv lignin by modeling the gas and tar evolution as well as the oligomer distribution for pyrolysis at low heating rates under vacshyuum or 1 atm pressure This approach should work for most lignins because of the similarshyities in the gas and tar evolution kinetics and the oligomer types for a wide variety of lignins that have been pyrolyzed under similar conditions914

5 The key inputs required to model the decomposition of lignin are the network parameters depolymerization reacshytions crosslinking reactions gas formation reactions and tar transport mechanisms Additional work will be required to better establish the appropriate network parameters and cross linking reactions in order to chose between two different tar transport mechanshyisms The model also needs to be validated by testing its predictive capability against lignin pyrolysis data from a wide range of heating rates and pressures

Acknowledgements-This work was supported by the United States Department of Energy under Contract No DE-FG05-90ER80880 The authors also acknowledge helpful discussions with Dr Helena Chum of the Solar Energy Research Institute (Golden CO) and Dr Jairo Lora of Repap Technologies Inc (Valley FOIge PA) who also supplied the ALC lignin Dr Ripudmnan Malhotra of SRI International (Menlo Park CA) assisted with the interpretation of the pyrolysis-FIMS data

REFERENCES

I WG Glasser Lignin in Fundamentals of Thermochemical Biomass Conversion (RP Overeshynd TA Milne and LK Mudge Eds) pp61-76 Elsevier Applied Science London (1985)

2 SL Falkehag Appl Polym Symp28 247-257 (1975)

3 WG Glasser and S Sarkanen Lignin Properties and Materials ACS Symposium Series No 397 American Chemical Society Washington DC (1989)

4 K Kringstad Future Sources of Organic Raw Materials-CHEMRA WN (LE St-Pierre and GR Brown Eds) pp 627middot636 Pergamon Press New York (1980)

5 EJ Sokes In Pyrolysis Oils from Biomass (E1 Soltes and TA Milne Eds) pp 1-5 ACS Symposhysium Series No 376 American Chemical Society Washington DC (1988)

6 MJ AntalJr In Advances in Solar Energy (KW Boer and JW Duffie Eds) Vol 2 p 175 Amerimiddot can Solar Energy Society Boulder CO (1985)

7 TR Nunn JB Howard JP Longwell and WA Peters Product compostions and kinetics in the rapid pyrolysis of milled wood lignin Ind Engng Chern Process Des Dev 24 844-852 (1985)

8 MT Klein and PS Virl Ind Engng Chern Fundam 22 35 (1983)

9 E Avni RW Coughlin PR Solomon and H-H King Lignin pyrolysis in a heated grid apparatus experiment and theory ACS Division of Fuel Chemistry Preprints2B5) 307 (1983)

10 H-H King and PR Solomon E Avni and R Coughlin Modeling tar composition in lignin pyrolysisACS Division ofFuel Chemistry Preprishynts 28(5) 319 (1985)

II FP Petrocelli and MT Klein Simulation of Kraft lignin pyrolysis In Fundamentals of Thennochemical Biomass Conversion (RP Overeshynd TA Milne and LK Mudge Eds) pp 257-273 Elsevier New York (1985)

12 PM Train and MT Klein Chemical modeling of lignin In Pyrolysis Oils from Biomass (E] Soltes and TA Milne FAs) pp 241-263 ACS Symposhysium Series No 376 American Chemical Society Washington DC (1988)

13 A Nigmn and MT Klein A mechanism-oriented lumping strategy for heavy hydrocaroon pyrolysis imposition of quantitative structure-reactivity relationships for pure components Ind Engng Chern Res 321297-1303 (1993)

14 KR Squire and PR Solomon Characterization of biomass as a source of chemicals Final Report to NSF for Contract No CPE-8107453 (Nov 1983)

15 MA Serio E Kroo R Bassilakis PR Solomon and R Malhotra Innovative methods for the production of polymers from biobased materials Final Report to DOE for Contract No DE-FG05shy9OER80880 (May 1991)

16 MA Serio E Kroo R Bassilakis PR Solomon

124 M A SERIo et al

and R Malhotra Production of carbon materials from biomass ACS Div ofFuel Chern Preprints 36(3) 1110 (1991)

17 PR Solomon and H-H King Pyrolysis of coal and model polymers theory and experiment Fuel 63 1302 (1984)

18 KR Squire PR Solomon RM Carangelo and MB DiTaranto Tar evolution from coal and model polymers n the effects of aromatic ring sizes and donatable hydrogens Fuel 65 833 (1986)

19 PR Solomon DG Hamblen RM Carangelo MA Serio and GV Deshpande A general model for coal devolatilization Energy and Fuels 2 405 (1988)

20 TA Milne HA Chum P Agblevor and DK Johnson Standardized analytical methods Biomass andBioenergy2(l-6) 341-366 (1992)

21 JH Lora cP Wu EK Pye and JJ Balatinecz Characteristics and potential applications of lignin produced by an organosolv pulping process In Lignin Properties and Materials (WG Glasser and S Sarkanen Eds) Chap 23 pp 312-323 ACS Symposium Series No 397 American Chemical Society Washington DC (1989)

22 JK Whelan PR Solomon GV Deshpande and RM Carangelo Thermogravimetric Fourier Transshyform Infrared spectroscopy (TG-FfIR) of petroshyleum source rock-initial results Energy and Fuel 2 65 (1988)

23 JB Howard WA Peters and MA Serio Coal devolatilization information for reactor modeling

Report prepared under EPRI AP-1803IRP 986-5 (April 198 1)

24 DC Elliott In Pyrolysis Oils from Biomass Producing Analyzing and Upgrading(EJ Soltes and TA Milne Eds) pp SS-6S ACS Symposium Series No 376 American Chemical Society Washington DC (1988)

25 GA St John SE Bulrill Jr and M Anbar ACS Symposium Series No 71 p 223 American Chemical Society Washington DC (1978)

26 ERE van der Hage M Mulder and JJ Boon Structural characterization of lignin polymers by temperature resolved in-source pyrolysis-mass spectrometry and Curie-point pyrolysis-gas chroshymatographymass spectrometry J Anal Appl Pyrolysis 25 149-183 (1993)

27 MT Klein Model pathways in lignin thermolysis ScD Thesis Massachusetts Institute of Technolshyogy Cambridge MA (1981)

28 GG Allan and T Matilla Lignins Occurrence FOnlUltion Structure and Reactions(KV Sarkanen and CH Ludwig Eds) Chap 14 Wiley New York (1971)

29 MA Serio DG Hamblen JR Markham and PR Solomon Kinetics of volatile product evolution in coal pyrolysis experimental and theory Energy and Fuels 1(2) 138 (1987)

30 PR Solomon DG Hamblen MA Serio Z-Z Yu and S Charpenay A characterization method and model for predicting coal conversion behavior Fuel 72 469 (1993)

123 Measurement and modling of lignin pyrolysis

The mean activation energy for bridge breaking which gave the best results for case B is only slightly different from that in the preceding case (50 kcaJ instead of 49 kcaJ) The difference is simply due to the fact that the vaporization process is a convolution of the bridge breaking and transport processes so that changing one will require changing the other in order to simulate a given data set

In conclusion both Cases A and B present relatively good simulations of the available data The limitations observed (oligomers evolution temperature too high for Case A and lack of increase in tar with heating rate for Case B) should be reduced with a better treatment of tar transport mechanisms

CONCLUSIONS

The FG-DVC model demonstrated its ability to represent the thermal decomposition of an organosolv lignin by modeling the gas and tar evolution as well as the oligomer distribution for pyrolysis at low heating rates under vacshyuum or 1 atm pressure This approach should work for most lignins because of the similarshyities in the gas and tar evolution kinetics and the oligomer types for a wide variety of lignins that have been pyrolyzed under similar conditions914

5 The key inputs required to model the decomposition of lignin are the network parameters depolymerization reacshytions crosslinking reactions gas formation reactions and tar transport mechanisms Additional work will be required to better establish the appropriate network parameters and cross linking reactions in order to chose between two different tar transport mechanshyisms The model also needs to be validated by testing its predictive capability against lignin pyrolysis data from a wide range of heating rates and pressures

Acknowledgements-This work was supported by the United States Department of Energy under Contract No DE-FG05-90ER80880 The authors also acknowledge helpful discussions with Dr Helena Chum of the Solar Energy Research Institute (Golden CO) and Dr Jairo Lora of Repap Technologies Inc (Valley FOIge PA) who also supplied the ALC lignin Dr Ripudmnan Malhotra of SRI International (Menlo Park CA) assisted with the interpretation of the pyrolysis-FIMS data

REFERENCES

I WG Glasser Lignin in Fundamentals of Thermochemical Biomass Conversion (RP Overeshynd TA Milne and LK Mudge Eds) pp61-76 Elsevier Applied Science London (1985)

2 SL Falkehag Appl Polym Symp28 247-257 (1975)

3 WG Glasser and S Sarkanen Lignin Properties and Materials ACS Symposium Series No 397 American Chemical Society Washington DC (1989)

4 K Kringstad Future Sources of Organic Raw Materials-CHEMRA WN (LE St-Pierre and GR Brown Eds) pp 627middot636 Pergamon Press New York (1980)

5 EJ Sokes In Pyrolysis Oils from Biomass (E1 Soltes and TA Milne Eds) pp 1-5 ACS Symposhysium Series No 376 American Chemical Society Washington DC (1988)

6 MJ AntalJr In Advances in Solar Energy (KW Boer and JW Duffie Eds) Vol 2 p 175 Amerimiddot can Solar Energy Society Boulder CO (1985)

7 TR Nunn JB Howard JP Longwell and WA Peters Product compostions and kinetics in the rapid pyrolysis of milled wood lignin Ind Engng Chern Process Des Dev 24 844-852 (1985)

8 MT Klein and PS Virl Ind Engng Chern Fundam 22 35 (1983)

9 E Avni RW Coughlin PR Solomon and H-H King Lignin pyrolysis in a heated grid apparatus experiment and theory ACS Division of Fuel Chemistry Preprints2B5) 307 (1983)

10 H-H King and PR Solomon E Avni and R Coughlin Modeling tar composition in lignin pyrolysisACS Division ofFuel Chemistry Preprishynts 28(5) 319 (1985)

II FP Petrocelli and MT Klein Simulation of Kraft lignin pyrolysis In Fundamentals of Thennochemical Biomass Conversion (RP Overeshynd TA Milne and LK Mudge Eds) pp 257-273 Elsevier New York (1985)

12 PM Train and MT Klein Chemical modeling of lignin In Pyrolysis Oils from Biomass (E] Soltes and TA Milne FAs) pp 241-263 ACS Symposhysium Series No 376 American Chemical Society Washington DC (1988)

13 A Nigmn and MT Klein A mechanism-oriented lumping strategy for heavy hydrocaroon pyrolysis imposition of quantitative structure-reactivity relationships for pure components Ind Engng Chern Res 321297-1303 (1993)

14 KR Squire and PR Solomon Characterization of biomass as a source of chemicals Final Report to NSF for Contract No CPE-8107453 (Nov 1983)

15 MA Serio E Kroo R Bassilakis PR Solomon and R Malhotra Innovative methods for the production of polymers from biobased materials Final Report to DOE for Contract No DE-FG05shy9OER80880 (May 1991)

16 MA Serio E Kroo R Bassilakis PR Solomon

124 M A SERIo et al

and R Malhotra Production of carbon materials from biomass ACS Div ofFuel Chern Preprints 36(3) 1110 (1991)

17 PR Solomon and H-H King Pyrolysis of coal and model polymers theory and experiment Fuel 63 1302 (1984)

18 KR Squire PR Solomon RM Carangelo and MB DiTaranto Tar evolution from coal and model polymers n the effects of aromatic ring sizes and donatable hydrogens Fuel 65 833 (1986)

19 PR Solomon DG Hamblen RM Carangelo MA Serio and GV Deshpande A general model for coal devolatilization Energy and Fuels 2 405 (1988)

20 TA Milne HA Chum P Agblevor and DK Johnson Standardized analytical methods Biomass andBioenergy2(l-6) 341-366 (1992)

21 JH Lora cP Wu EK Pye and JJ Balatinecz Characteristics and potential applications of lignin produced by an organosolv pulping process In Lignin Properties and Materials (WG Glasser and S Sarkanen Eds) Chap 23 pp 312-323 ACS Symposium Series No 397 American Chemical Society Washington DC (1989)

22 JK Whelan PR Solomon GV Deshpande and RM Carangelo Thermogravimetric Fourier Transshyform Infrared spectroscopy (TG-FfIR) of petroshyleum source rock-initial results Energy and Fuel 2 65 (1988)

23 JB Howard WA Peters and MA Serio Coal devolatilization information for reactor modeling

Report prepared under EPRI AP-1803IRP 986-5 (April 198 1)

24 DC Elliott In Pyrolysis Oils from Biomass Producing Analyzing and Upgrading(EJ Soltes and TA Milne Eds) pp SS-6S ACS Symposium Series No 376 American Chemical Society Washington DC (1988)

25 GA St John SE Bulrill Jr and M Anbar ACS Symposium Series No 71 p 223 American Chemical Society Washington DC (1978)

26 ERE van der Hage M Mulder and JJ Boon Structural characterization of lignin polymers by temperature resolved in-source pyrolysis-mass spectrometry and Curie-point pyrolysis-gas chroshymatographymass spectrometry J Anal Appl Pyrolysis 25 149-183 (1993)

27 MT Klein Model pathways in lignin thermolysis ScD Thesis Massachusetts Institute of Technolshyogy Cambridge MA (1981)

28 GG Allan and T Matilla Lignins Occurrence FOnlUltion Structure and Reactions(KV Sarkanen and CH Ludwig Eds) Chap 14 Wiley New York (1971)

29 MA Serio DG Hamblen JR Markham and PR Solomon Kinetics of volatile product evolution in coal pyrolysis experimental and theory Energy and Fuels 1(2) 138 (1987)

30 PR Solomon DG Hamblen MA Serio Z-Z Yu and S Charpenay A characterization method and model for predicting coal conversion behavior Fuel 72 469 (1993)

124 M A SERIo et al

and R Malhotra Production of carbon materials from biomass ACS Div ofFuel Chern Preprints 36(3) 1110 (1991)

17 PR Solomon and H-H King Pyrolysis of coal and model polymers theory and experiment Fuel 63 1302 (1984)

18 KR Squire PR Solomon RM Carangelo and MB DiTaranto Tar evolution from coal and model polymers n the effects of aromatic ring sizes and donatable hydrogens Fuel 65 833 (1986)

19 PR Solomon DG Hamblen RM Carangelo MA Serio and GV Deshpande A general model for coal devolatilization Energy and Fuels 2 405 (1988)

20 TA Milne HA Chum P Agblevor and DK Johnson Standardized analytical methods Biomass andBioenergy2(l-6) 341-366 (1992)

21 JH Lora cP Wu EK Pye and JJ Balatinecz Characteristics and potential applications of lignin produced by an organosolv pulping process In Lignin Properties and Materials (WG Glasser and S Sarkanen Eds) Chap 23 pp 312-323 ACS Symposium Series No 397 American Chemical Society Washington DC (1989)

22 JK Whelan PR Solomon GV Deshpande and RM Carangelo Thermogravimetric Fourier Transshyform Infrared spectroscopy (TG-FfIR) of petroshyleum source rock-initial results Energy and Fuel 2 65 (1988)

23 JB Howard WA Peters and MA Serio Coal devolatilization information for reactor modeling

Report prepared under EPRI AP-1803IRP 986-5 (April 198 1)

24 DC Elliott In Pyrolysis Oils from Biomass Producing Analyzing and Upgrading(EJ Soltes and TA Milne Eds) pp SS-6S ACS Symposium Series No 376 American Chemical Society Washington DC (1988)

25 GA St John SE Bulrill Jr and M Anbar ACS Symposium Series No 71 p 223 American Chemical Society Washington DC (1978)

26 ERE van der Hage M Mulder and JJ Boon Structural characterization of lignin polymers by temperature resolved in-source pyrolysis-mass spectrometry and Curie-point pyrolysis-gas chroshymatographymass spectrometry J Anal Appl Pyrolysis 25 149-183 (1993)

27 MT Klein Model pathways in lignin thermolysis ScD Thesis Massachusetts Institute of Technolshyogy Cambridge MA (1981)

28 GG Allan and T Matilla Lignins Occurrence FOnlUltion Structure and Reactions(KV Sarkanen and CH Ludwig Eds) Chap 14 Wiley New York (1971)

29 MA Serio DG Hamblen JR Markham and PR Solomon Kinetics of volatile product evolution in coal pyrolysis experimental and theory Energy and Fuels 1(2) 138 (1987)

30 PR Solomon DG Hamblen MA Serio Z-Z Yu and S Charpenay A characterization method and model for predicting coal conversion behavior Fuel 72 469 (1993)