Slow Pyrolysis of Woody Residues and an Herbaceous Biomass Crop

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Ind. Eng. Chem. Res. 2005, 44, 6650-6660

Slow Pyrolysis of Woody Residues and an Herbaceous Biomass Crop: A Kinetic Study Claudia J. Go´ mez,† Ga´ bor Va´ rhegyi,‡ and Luis Puigjaner*,† Department of Chemical Engineering-ETSEIB, Universitat Polite` cnica de Catalunya, Avinguda Diagonal, 647, E-08028-Barcelona, Spain, and Institute of Materials and Environmental Chemistry, Chemical Research Center, Hungarian Academy of Sciences, P.O. Box 17, Budapest, 1525 Hungary

The devolatilization behavior of pine and beech wood from carpentry residues and an herbaceous product from an energy plantation (artichoke thistle) was investigated by thermogravimetry. The effect of three pretreatments, hot-water washing, ethanol extraction, and a combination, were also studied. Various approaches based on first and nth order partial reactions in a summative model of pseudocomponents were employed to determine the best kinetic parameters that describe the experiments both at linear and stepwise heating programs. The assumption of four partial reactions appropriately described the global decomposition of most of the samples, while a low-temperature partial reaction was needed for the untreated pine. After the separate kinetic parameters for each of the samples were determined, a further check on the effects of the pretreatments was carried out by determining how a common set of activation energies describes the untreated and pretreated samples of the same feedstock. 1. Introduction Pyrolysis and other thermochemical conversion processes represent an important option for the utilization of biomass and waste. The increasing dependence on imported oil as well as the urgency to reduce greenhouse emissions are some of the reasons that justify an energy policy that carefully considers the role of renewable sources as energy carriers. The upsurge of interest in the simulation and optimization of the reactors for thermochemical processes requires appropriate models that integrate different operational conditions and varied feedstocks and help to achieve a better understanding of the reactions in the corresponding processes. Numerous researchers have intensively investigated the kinetics of cellulose and biomass pyrolysis for nearly half a century (see, for example, the review of Antal and Va´rhegyi1). Thermogravimetry has proved to be a useful tool in elucidating the decomposition of various biomass materials. Widely different kinetic parameters have been published in the literature because of the greatly different experimental conditions, various experimental problems, and the occasional use of unsuitable evaluation methods. In the recent years, a consensus on the identification of the primary decomposition processes and the appropriate requirements for the kinetic control in the thermoanalytical experiments has emerged. Current works aim at establishing simple unified mechanisms that describe the behavior of the samples in a wide range of experimental conditions and take into account the systematic experimental errors. Antal et al.2 proved that the volatilization process of several cellulose samples under different heating rates could be well represented by a simple single-step irreversible firstorder rate law with a single value of activation energy, *To whom correspondence should be addressed. E-mail: [email protected]. Tel.: +34 934 016 678. Fax: +34 934 010 979. † Universitat Polite`cnica de Catalunya. ‡ Hungarian Academy of Sciences.

Figure 1. Heating programs for the kinetic studies.

whereas the different values of log A accounted for variations in the systematic thermal lag at high heating rates and for the inherent differences in the cellulose samples. Grønli et al.,3 Va´rhegyi et al.,4 and Me´sza´ros et al.5 showed that different biomass samples or a given sample with different pretreatments can be described by similar kinetic parameters. In the attempt to identify the zones associated with the devolatilization of the biomass components and their kinetics, different T(t) heating programs have been employed. The temperature domains of moisture evolution and hemicellulose, cellulose, and lignin decomposition more or less overlap each others. The chemical heterogeneities of the biomass components and the physical heterogeneity of the plant materials increase this overlap. Consequently, superposed reactions could be unnoticed if the evaluation is based only on linear heating programs. Me´sza´ros et al.5 increased the information content of the experiments by involving successive isothermal steps (stepwise heating programs) in their study. This approach required more partial peaks than the studies based only on linear T(t) programs.6-12

10.1021/ie050474c CCC: $30.25 © 2005 American Chemical Society Published on Web 07/22/2005

Ind. Eng. Chem. Res., Vol. 44, No. 17, 2005 6651

Figure 2. Comparison between the observed (O) and simulated (solid line) DTG curves at 20 °C/min (1) and at a stepwise temperature program (dashed line in panel 2) employing the first-order model (see Table 2 for the description of the simulated partial reactions).

The increased exploitation of biomass materials different from those usually investigated requires a better description of both the dynamics and the influence of the heterogeneities on biomass thermolysis. To study the influences of feedstock properties, special attention has been given to the effects of pretreating the materials on the kinetics. In an early paper, Va´rhegyi et al.13 showed that the mineral matter present in the biomass

samples can highly increase the overlap of the partial peaks in the DTG curves. Later, several other studies showed the ability of pretreatments to separate merged peaks, displace reaction zones toward higher temperatures, decrease char yield, and increase peak reaction rates.4,8,11,12,14,15 At this time, the understanding of the effects of pretreatments remains largely qualitative. The various industrial applications (e.g., the production of

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Ind. Eng. Chem. Res., Vol. 44, No. 17, 2005

Table 1. Results of the Evaluation by the First-Order Model pseudocomponent untreated pine washed pine untreated beech washed beech untreated thistle washed thistle

(s-1)

log A E (kJ/mol) c log A (s-1) E (kJ/mol) c log A (s-1) E (kJ/mol) c log A (s-1) E (kJ/mol) c log A (s-1) E (kJ/mol) c logA (s-1) E (kJ/mol) c

ia

1

2

3

4

fitb (%)

5.73 77 0.02

11.86 149 0.10 11.80 148 0.10 11.72 148 0.12 12.12 153 0.12 8.67 109 0.15 7.57 100 0.13

13.95 181 0.12 14.35 185 0.11 13.67 177 0.10 14.17 183 0.09 10.86 145 0.28 9.34 127 0.15

12.96 183 0.45 13.50 190 0.50 12.58 178 0.49 13.02 185 0.52 13.51 185 0.12 13.20 182 0.34

7.96 137 0.08 7.65 134 0.07 8.22 140 0.06 8.29 142 0.05 4.85 95 0.12 4.52 92 0.10

2.18 3.23 2.68 4.04 3.77 1.68

a A low-temperature process observed at the untreated pine sample. b Overall fit on the simultaneous evaluation of both linear and stepwise heating experiments.

Table 2. Partial Curves in the Kinetic Decomposition and Their Notation in the Figures name and description i: thermally labile groups of extractives, hemicellulose, and lignin pseudocomponent 1: first main peak of hemicellulose (and some devolatilization of extractives and lignin) pseudocomponent 2: second main peak of hemicellulose (and some devolatilization of extractives and lignin) pseudocomponent 3: cellulose peak of hemicellulose (and some devolatilization of extractives and lignin) pseudocomponent 4: lignin’s main peak

notation ××× s - - +++ - - -

charcoal, activated carbon, and liquid fuels) require a clear understanding of the variations in the original composition and the effects of the pretreatments because the yields and the composition, structure, and other properties of the products are highly influenced by the properties of the feedstock.16-19 The present work aims for the clarification of the thermal properties of important feedstocks that have not yet attracted sufficient attention in biomass studies. The woody residues of the carpentry industry remain largely unused and present a solid waste disposal problem. Thistle, produced in an energy plantation, is already used as a raw material for an electric power generation plant in Spain, although much remains to be learned about its thermochemical properties. In a previous work, these biomasses were investigated with a 20 °C/min linear heating program, and the experiments were evaluated by assuming three pseudocomponents.12 The present work extends these investigations in two ways: (i) linear and stepwise temperature programs were measured and evaluated and (ii) the range of the evaluated samples has been extended by involving ethanol-extracted samples and samples subjected to both water-washing and ethanol extraction. In this way, the kinetic description of the results is based on a larger set of experimental information and valid for a wider range of experimental conditions. 2. Experimental Section The wood chips (pine and beech) were taken from Barcelona’s carpentry residues, while the thistle sample

(cynara cardunculus) came from a specialized crop, developed for investigation in the Spanish province of Soria. The pretreatment procedures and the analytical characteristics of the treated and pretreated samples have been presented earlier.12 A Setaram Setsys 12 thermobalance was employed for the experiments. It consists of a vertical balance with a 0.03 µg resolution. For the kinetic studies various heating programs were used, as shown in Figure 1, under atmospheric pressure with an initial sample mass of 4 mg. Particles of sizes below 0.1 mm were distributed evenly in an open platinum crucible with a diameter of 4.5 mm. A nitrogen gas flow was employed with a nominal gas velocity of around 0.005 m/s. The buoyancy effect was corrected by subtracting the TG signal of experiments with empty sample holders. The FORTAN 90 and C++ programs developed by one of the authors2,3,7,9,20 were employed. The differential equations of the model were solved by a high precision (10-10) numerical solution. The least-squares parameters were determined by a modified HookJeeves minimization, which is a safe and stable direct search method. Each minimization was repeatedly restarted from the optimum found in the previous run of the algorithm until no further improvement was achieved. 3. Kinetic Modeling of Thermal Decomposition 3.1. Model of Pseudocomponents. The nonisothermal thermogravimetric analysis of the biomass materials provides more or less overlapped peaks for the cellulose and hemicellulose components, while the lignin and extractives contribute to the mass loss in wider temperature domains. An inherent difficulty exists in the deconvolution of the DTG curves because of the high variability of these compounds from one biomass species to another. A practical method for the mathematical description is to assume that the pseudocomponents formed by fractions of the biomass components decompose in a similar manner in similar temperature ranges. For each pseudocomponent, a conversion (reacted fraction), Rj, and a reaction rate, dRj/dt, are defined (see the

Ind. Eng. Chem. Res., Vol. 44, No. 17, 2005 6653 Table 3. Results of the Evaluation Assuming n4 ) 3 pseudocomponent log A (s-1) E (kJ/mol) c n log A (s-1) E (kJ/mol) c n log A (s-1) E (kJ/mol) c n log A (s-1) E (kJ/mol) c n log A (s-1) E (kJ/mol) c n log A (s-1) E (kJ/mol) c n

untreated pine

washed pine

untreated beech

washed beech

untreated thistle

washed thistle

i

1

2

3

4

fit (%)

5.82 78 0.02 1

11.88 150 0.10 1 11.81 149 0.10 1 11.78 148 0.12 1 12.16 153 0.12 1 8.82 111 0.15 1 7.63 101 0.14 1

14.07 182 0.11 1 14.43 186 0.11 1 13.71 177 0.10 1 14.21 183 0.09 1 11.05 146 0.26 1 9.57 130 0.15 1

13.01 183 0.46 1 13.53 190 0.50 1 12.64 179 0.50 1 13.04 185 0.53 1 14.77 197 0.11 1 13.34 184 0.33 1

14.14 215 0.08 3 16.18 244 0.07 3 16.08 241 0.06 3 20.89 307 0.05 3 8.75 138 0.16 3 7.72 130 0.14 3

1.69

Nomenclature section). The overall reaction rate is a linear combination of these partial reactions

dm

/dt )

cj dRj/dt ∑ j)1

(1)

where M is the number of the pseudocomponents and cj is the fraction of the overall mass loss from the jth pseudocomponent. Each partial reaction is approximated by an Arrhenius equation

dRj/dt ) Aj exp(-Ej/RT)(1 - Rj)nj

(2)

Here Aj is the pre-exponential factor, Ej is the apparent activation energy, and nj is the reaction order. 3.2. Temperature Programs for the Kinetic Evaluation. Two types of heating programs (linear and stepwise) are used in this work (Figure 1). The stepwise temperature programs have been included to obtain enough information for the reliable determination of the unknown parameters. The corresponding steps were selected for each interesting region in the pyrolysis process: at the low-temperature phenomena, at the start of the main hemicellulose decomposition, and at the start of the main cellulose decomposition. The same temperature program was employed for both woods. In the case of thistle, lower temperature steps were used because the devolatilization takes place at lower T values.12 The stepwise experiments took place in longer time intervals than the ones with linear temperature programs. Their DTG peak maxima were 10-70% of the values of the corresponding linear T(t) experiments at 20 °C/min. Accordingly, the peak values of the heat flux required by the endothermic reaction were also much lower in the stepwise experiments. An additional linear heating program, 40 °C/min, was introduced to clarify the unusual behavior of the herbaceous biomass. 3.3. Evaluation by the Method of Least Squares. The unknown parameters of the model were determined

2.37

3.83

3.11

1.43

by the simultaneous evaluation of more than one experiment for each sample Nexp Np

M

calc

3.04

S)

∑∑

p)1 i)1

[( ) dm dt

obs p

(ti) -

( ) ] dm dt

calc p

2

(ti) /Np/hp2

(3)

Subscript p indicates the heating programs, Nexp is the number of experiments evaluated simultaneously, ti denotes the time values in which the digitized (dm/dt)obs values were taken, and Np is the number of the ti points in a given experiment. hp denotes the heights of the evaluated curves [hp ) max(-dm/dt)obx p ]. The division by hp serves for normalization. The DTG curves were evaluated by the method of least squares because they are more sensitive to subtle changes in the mechanism than the TG curves. The DTG curves were determined by spline smoothing.21 The root-mean-square difference between the TG curves and the smoothing spline was small, 0.5 µg (∼0.01%); accordingly, the differentiation did not introduce considerable systematic error into the evaluation. For each group of experiments evaluated simultaneously, a fit quantity was calculated

fit(%) ) 100(S/Nexp)-1/2

(4)

When the fit for a single experiment is given, the same formula is used with Nexp ) 1. Similar formulas can be used to express the repeatability of the experiments in quantitative form. In this case, the root-mean-square difference between two repeated experimental DTG curves is calculated and normalized by the peak maximum. In this manner, we got an average relative error of 1% for the repeatibility of the nonisothermal experiments. 4. Results and Discussion 4.1. Model With First-Order Kinetics. The simplest form of the employed model is the approximation of the kinetics by first order reactions. The partial curves and the individual fit between the experimental

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Figure 3. Comparison between the observed (O) and simulated (solid line) DTG curves at 20 °C/min (1) and at a stepwise temperature program (dashed line in panel 2) employing a third-order model for the last pseudocomponent (see Table 2 for the description of the simulated partial reactions).

and simulated DTG curves are shown in Figure 2 for a selection of the experiments. Note that both the linear and stepwise heating programs of the study belonged

in the range of slow pyrolysis, accordingly the same kinetics and mechanism were assumed for all experiments. In the present work, four or five reactions,


depending on the sample, were necessary to describe the characteristics of the observed DTG curves. The complete characterization of the untreated pine decomposition involved one additional partial reaction in the low-temperature region (around 250 °C), while the other samples did not show considerable mass loss in this range. The obtained parameters and fit values for the untreated and washed samples are summarized in Table 1. As the results show, the stepwise and linear heating rate experiments can be described by the same kinetic parameters, indicating that the higher heat flux demand of the linear T(t) experiments did not result in significant heat transfer problems. On the basis of the very broad decomposition range of the lignin3 and extractives,22 a contribution from these components is expected for all partial peaks. The first domain obtained for untreated pine corresponds mainly to the thermally labile functional groups of the extractives and hemicellulose, in agreement with the high content of extractives in this sample. The next peaks, around 300 °C and 330 °C, are dominated by the main hemicellulose decomposition, in accordance with other works reporting double hemicellulose peaks on the DTG curves of lignocellulosic materials.5,9,23 The sharp high peak at around 370 °C is characteristic of the cellulose pyrolysis. The last peak, around 440 °C, corresponds mainly to lignin thermolysis.3,24,25 The thistle sample differed from the woods (see panel C in Figure 2). This may be partly the result of the chemical differences between the constituents of an herbaceous plant and a wood and partly the result of the much higher ash content. The effect of the mineral matter is well reflected by the lower overlapping of the partial peaks on the water-washed thistle, as shown in panel D of Figure 2. The resulting kinetic parameters are close to the ones presented by Teng et al. for the cellulose and hemicellulose components in the pyrolysis of untreated rice hulls,6,10 as well as those obtained by Me´sza´ros et al. for short rotation forest biomass.5 It is interesting to note that higher activation energies for the cellulose decomposition are frequently reported in the literature.1,26 The lower cellulose activation energies obtained in the present work may be the result of several factors. The involvement of the stepwise temperature program requires values that describe the decomposition in a broader range of experimental conditions. Chemical or physical inhomogeneities in the cellulose components may also lead to a broader temperature domain. From a mathematical point of view, broader DTG peaks can be described by lower E values. There is also a variation in the activation energies for different pure cellulose samples, even if they are determined at identical experimental conditions by the same evaluation techniques.2 The lowest activation energy reported for a sample of pure cellulose by Antal et al. is close to the results of this paper. 4.2. Model With Third-Order Kinetics for the Last Pseudocomponent. The results of Manya` et al.11 and Go´mez et al.12 show that a better fit can be achieved if the behavior of the last pseudocomnent, dominated by the lignin decomposition, is described by a third-order reaction. This observation has been deduced from the evaluation of the constant-heating rate experiments. The present work extends this result to the simultaneous evaluation of experiments with linear and stepwise temperature programs. The parameters and fits obtained by assum-

Figure 4. Comparison between the observed (O) and simulated (solid line) DTG curves for untreated thistle at 40 °C/min employing a third-order model for the last pseudocomponent and the same kinetic parameters determined at 20 °C/min.

ing n4 ) 3 are shown in Table 3 and Figure 3. The greatest improvement appeared in the fit of the thistle sample. The kinetic parameters of the last pseudocomponent were obviously changed, while the rest of the parameters were only slightly affected. The E4 values obtained for thistle are close to the corresponding values of Me´sza´ros et al.,5 who used n ) 2 in their nth order model. For the wood component, however, much higher E4 values were obtained than those reported in the literature. As this pseudocomponent describes the hightemperature processes of the biomass decomposition and higher decomposition temperatures are usually the result of higher energy barriers, the values obtained in this paper may be closer to the true physical activation energies of these reactions than the values obtained from linear temperature programs only. 4.3. Prediction of a Higher Heating Rate Using the Model. A simple way to check the reliability of a model is to study how suitable it is to predict phenomena outside the domain used in the determination of its parameters. For this reason, we tested to see if the results obtained from the stepwise and 20 °C/min experiments can be used to predict the behavior of the samples at a higher heating rate, 40 °C/min. An acceptable fit was obtained, as shown in Figure 4, indicating the strength of the approaches used in our work. This sort of test has rarely been used in the leastsquares evaluation of the thermoanalytical curves of biomass materials. Va´rhegyi et al. predicted the behavior of stepwise heating programs from the results of 10 and 40 °C/min experiments in a charcoal study.27 Conesa et al.28 emphasized the importance of describing experiments with different temperature programs with exactly the same kinetic parameters as a method for distinguishing a robust model. Several other works stated, however, that highheating rate experiments cannot be described by the kinetic parameters obtained from lower-heating rate experiments because of heat transfer problems.1,2,5,7,29-31 In these investigations, both the sample and the equipment differ from the ones employed in the present work. In addition, the quoted works, with the exception of Me´sza´ros et al.,5 were reported on the basis of only linear heating rate experiments. The work of Stenseng et al.32 adds an interesting information about the samples. They observed that a herbaceous biomass

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Figure 5. DTG curves of untreated and pretreated pine (A) and thistle (B) samples at 20 °C/min. Table 4. Summarized Results of the Evaluation Using Activation Energies from the Best Kinetic Results for the Washed Samples pinea

thistle pseudocompenent 1st

parameters differencesb

2nd

parameters differences

3rd


4th


fit (%)

value differencec

washed extracted washed and extracted untreated washed extracted washed and extracted untreated washed extracted washed and extracted untreated washed extracted washed and extracted untreated washed untreated

log A (s-1)

E (kJ/mol)

7.63 0.15 -0.08 0.24 9.57 -0.05 -0.27 0.00 13.34 0.29 -0.04 0.23 7.72 0.16 -0.09 0.26

101

1.43 0.21

130

184

130

c

log A (s-1)

E (kJ/mol)

0.14 0.01 0.02 0.00 0.15 0.10 0.03 0.13 0.33 -0.17 -0.07 -0.21 0.14 0.00 -0.02 0.03

11.81 0.02 0.06 -0.05 14.43 0.03 0.03 -0.07 13.53 0.11 0.01 0.07 16.18 -0.01 -0.55 0.10

149

3.04 0.21

186

190

244

beech c 0.10 0.01 -0.01 0.01 0.11 0.01 0.01 0.00 0.50 -0.04 0.00 -0.06 0.07 0.00 0.00 0.01

log A (s-1)

E (kJ/mol)

12.16

153

0.12

0.03 14.21

183

0.01 0.09

0.02 13.04

185

0.01 0.53

0.11 20.89

307

-0.04 0.05

0.25

c

0.00

3.83 0.21

a The initial partial process for the untreated pine sample is not reported in this table. b Alterations from the parameters of the corresponding washed sample. c Alterations from the fit value of the prior best kinetic evaluation.

sample (straw) presents a much lower heat demand than pure cellulose (Avicel) and, in this way, is less affected by heat transfer intrusions during the experiments. 4.4. Description of the Experiments with the Activation Energies of the Water-Washed Samples. As described earlier, we studied the thermal decomposition of untreated, water-washed, and ethanol-extracted samples, as well as samples that were both waterwashed and ethanol extracted. The corresponding DTG curves are compared in Figure 5. The pretreatments had a much more pronounced effect on the thistle than on the wood because of the higher mineral matter and extractive content of the herbaceous sample translated into degradation taking place over a wider range and at lower temperatures. For all samples, the waterwashing had a stronger effect on the decomposition than the extraction because, in the latter case, only a slight displacement of the cellulose degradation toward higher temperatures was observed, indicating that the inorganic ions have a higher effect on the decomposition than the extractives. This observation is in accordance with earlier works.4,12,14,20 During the kinetic evaluation of these experiments we looked for an approach that emphasizes the similarities between the differently treated samples while expressing the differences in quantitative form, in terms

of the kinetic parameters. Taking into account the results of previous works,4 we employed the same set of activation energies for the pretreated and the untreated versions of a given sample with varying preexponential factors and cj coefficients. This approach can be visualized as follows. For nonisothermal experiments, an activation energy value approximately defines the width of the corresponding partial peak, while the main effect of a small variation in the corresponding log A value is the shifting of the temperature domain of the given peak. The variation of the cj coefficients reflects the change of the volatile matter production of the pseudocomponents. They are nearly proportional to the height of the curves when the E values are fixed and the log A values show only limited variation. We obtained favorable results by employing the activation energies of the water-washed experiments. Water washing does not considerably alter the chemical integrity of the sample1,6,33 and offers a better procedure for separating peaks than other pretreatments. The results are summarized in Table 4. The fit between the simulated and observed data is illustrated by Figures 6 and 7. In Table 4, the kinetic evaluation of the water washed samples provided the reference values. In the other cases, the alterations from the corresponding reference values are given to provide an easy view of the differences between the water-washed and other


Figure 6. Comparison between the observed (O) and simulated (solid line) DTG curves for pine at 20 °C/min (1) and at a stepwise temperature program (dashed line in panel 2) employing a third-order model for the last pseudocomponent and the same E value for all the cases.

samples. Because of the repetition of several experiments, each value in Table 4 was deduced from 2-6 experiments. An additional evaluation of the untreated thistle sample under a 40 °C/min linear heating rate by the actual method has also been included for comparative purposes. The log A and cj parameters were formed by averaging, while the fits are the root-meansquare values of the corresponding experiments, as defined by eq 4.

It is interesting to note that using the activation energies of the water-washed experiments resulted only in a slight alteration of the fit values. In the case of the wood samples, only the c3 coefficient revealed a considerable variation, while the rest of the parameters showed only minor changes. Coefficient c3 expresses mainly the formation of the volatiles from the cellulose component. Its values were lower by a factor of about 0.9 in the untreated wood samples, indicating the

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Figure 7. Comparison between the observed (O) and simulated (solid line) DTG curves for thistle at 20 °C/min (A-C) and at 40 °C/min (D) employing a third-order model for the last pseudocomponent and the same E value for all the cases.

catalytic activity of that part of the mineral content which can be removed by water-washing. The lower c3 values correspond to a higher char formation from the cellulose component of the untreated samples. The corresponding values of the untreated and extracted samples were similar, indicating a limited effect of the extraction on the volatile matter contribution. The pre-exponential factors showed only minor alterations except for the last partial peak. Note that the last partial peak is low, wide, and highly overlapped by more prominent peaks. Accordingly, its higher variation may be the result of a mathematical ill-definition of its determination, in accordance with the experimental uncertainty values reported by Va´rhegyi et al.4 Because the results also include the evaluation of the stepwise experiments, they extend the earlier results on the role of extraction and water-washing4,12-14 into a wider domain of experimental conditions. The situation is more complex in the case of the thistle. The highest alterations were observed at the second and third pseudocomponents. In the case of untreated thistle, a considerable part of the cellulose decomposition occurred in an unusually low-temperature domain and formally merged into the second pseudocomponent. The water washing diminished this strange behavior, and the partial curves obtained (panel D1 in Figures 2 and 3) are similar to those of a regular biomass sample. The data in Table 4 reflect this behavior. In the untreated sample, c2 was higher by 0.13 while c3 was lower by 0.21 than the corresponding values of the water-washed sample. The sum of the cj coefficients indicates that more char is formed from the

untreated sample, in agreement with the higher residue observed during the thermogravimetric experiments. It may be interesting to note that the magnitude of the first and last partial peaks had only minor changes while the corresponding log A values had considerable alterations. The cj values and the considerations above suggest that the change in the char yield can be connected mainly to the changes in the cellulose decomposition, in the same manner as in the woody samples. 5. Conclusions The devolatilization behavior of three biomass materials (pine and beech from carpentry residues and thistle from an energy plantation) was investigated within the range of slow pyrolysis, including various pretreatments to eliminate inorganic matter and extractives. Linear and stepwise temperature programs were employed. For each type of material three related approaches, based on the pseudocomponents model, were applied to determine the best kinetic parameters and to learn about the effect of the pretreatments. The assumption of four partial reactions, related to the domains of hemicellulose, cellulose, and lignin thermolysis, was necessary for a description of the global mass loss rates of the samples. In the case of untreated pine, an additional low-temperature partial peak was also included. Its presence may be connected to the high amount of extractives in this sample. In all of the approaches, linear and stepwise heating experiments were satisfactorily well described by the same set of


kinetic parameters for a given sample. In an earlier work, the decomposition of the same samples was described with linear heating programs by assuming three partial peaks. The higher number of partial peaks in the present work may be the result of the involvement of the stepwise heating programs, allowing the accumulation of kinetic information about the chemical heterogeneity of the samples, in a wider domain of experimental conditions. Previous works, based on linear temperature programs, established that the assumption of third-order kinetics for the high-temperature phenomena, dominated by the decomposition of lignin and the secondary reactions of the residues from the hemicellulose and cellulose, give a better overall fit. The present work extended this result to the simultaneous evaluation of linear and stepwise heating programs. In a test on the thistle sample, we got good results for predicting the behavior of high-heating rate experiments from the obtained kinetic parameters. The analysis of the various pretreatment effects was included in a further approach. We applied the same set of activation energies in the kinetic evaluation of the pretreated and the untreated behaviors of a given sample with varying pre-exponential factors and cj coefficients. This approach helped in the characterization of the pretreatment effects in quantitative terms. The data obtained in this manner showed that the increased volatilization in the water-washed samples is the result of the cellulose component. The results revealed a peculiar behavior of the cellulose component in the thistle sample. Water-washing proved to be an effective pretreatment for the deconvolution of the partial reactions in the kinetic analysis. The work demonstrated that the assumed reaction kinetics are not only suitable for describing the dynamics of a given sample in a particular experimental case but also capable of revealing the actual changes in the samples in a wider range of operational conditions. Acknowledgment This work was supported by the Ministerio de Educacio´n Cultura y Deporte of Spain and by the Hungarian National Research Fund (OTKA T37705 and T37704). Financial support received from the European Community (Contract RFC-CR-04006) is also thankfully acknowledged. Nomenclature R ) reacted fraction of a pseudocomponent A ) pre-exponential factor (s-1) c ) normalized mass of volatiles formed from a pseudocomponent E ) activation energy (kJ/mol) h ) height of a dmobs/dt curve mcalc(t) ) normalized sample mass calculated from a model mobs(t) ) mass of the sample divided by the initial sample mass M ) number of pseudocomponents n ) reaction order Nexp ) number of experiments evaluated simultaneously Np ) number of evaluated data on an experimental curve R ) gas constant (8.3143 × 10-3 kJ mol-1 K-1) S ) least squares sum for a series of experiments t ) time (s) T ) temperature (°C, K)

Subscripts i ) digitized point on an experimental curve j ) pseudocomponent p ) temperature program

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Received for review April 20, 2005 Revised manuscript received June 22, 2005 Accepted June 23, 2005 IE050474C

Slow Pyrolysis of Woody Residues and an Herbaceous Biomass Crop

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