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Kinetics of Biomass Pyrolysis: a Reformulated Three-Parallel-Reactions Model Joan J. Manya` , Enrique Velo, and Luis Puigjaner* Department of Chemical Engineering, Universitat Polite` cnica de Catalunya, Av. Diagonal 647, ETSEIB, E-08028 Barcelona, Spain
The thermal decompositions of sugarcane bagasse and waste-wood samples are studied using thermogravimetric analysis. Assuming the addition of three independent parallel decompositions, these corresponding to three pseudocomponents linked to the hemicellulose, cellulose, and lignin, the weight loss associated with the pyrolysis process is simulated. First, an irreversible firstorder reaction model is assumed for each pseudocomponent. Results show that the modelsimulated curves do not fit well to the experimental data. Consequently, a further kinetic study is carried out for the pure lignin (Kraft alkali lignin), which demonstrates that the pyrolysis of lignin is better described by a third-order reaction rate law. The reformulation of the lignin kinetic model, and its subsequent implementation in the summative model (for the third pseudocomponent), has allowed one to reach a good agreement between simulated and experimental data. Introduction Thermochemical conversion processes (gasification and pyrolysis, mainly) constitute very interesting options for biomass upgrading. The generation of a fuel that can be stored (gas or liquid) is a renewable alternative to the fossil fuels. The pyrolysis, basically a polymeric structure cracking process, converts the lignocellulosic materials into a volatile fraction and char. The volatile fraction (gas or liquid, depending on its molecular weight) can be used as a fuel or as a chemical synthesis source. On the other hand, the solid fraction presents several applications, like as a domestic fuel, in the production of activated carbon, or as a reducing agent in metallurgy.1 The kinetic study of the biomass pyrolysis is of relevant importance because it constitutes the initial step of combustion and gasification processes. The knowledge of the kinetics for the thermal decomposition of lignocellulosic materials is needed for the design of gasifiers and pyrolysis reactors. Antal and Va´rhegyi2 summarize the state of the art for the cellulose pyrolysis kinetics (the main component of the biomass). From diverse thermogravimetric studies (using pure cellulose, Avicel PH-105), they establish that the thermal degradation of the cellulose (using dynamic heating and under kinetic control and minimization of secondary reactions) is an endothermic process. Additionally, Antal and Va´rhegyi suggest that this process can be described by a single-step, first-order kinetic model with high activation energy (238 kJ/mol). Milosavljevic and Suuberg3 do not accept the conclusions of Antal and Va´rhegyi. They claim that the cellulose thermal degradation can be well described by a two-stage mechanism: the first at a low-temperature range with high activation energy (218 kJ/mol) and the second at a high-temperature range with reduced activation energy (140-155 kJ/mol). Antal et al.4 discuss the validity of the above-mentioned argument, consider* Corresponding author. Telephone: +34 3 4016678. Fax: +34 3 4017150. E-mail:
[email protected].
ing the experimental error of the thermogravimetric experiments performed by Milosavljevic and Suuberg excessively. The mass used for samples by Milosavljevic and Suuberg (30 mg) causes diffusion effects and, subsequently, an increase in the residence time for the vapor fraction, which promotes secondary reactions. Also, the thermal lag (between the thermocouple lecture and the real temperature of the sample) accentuates the compensation effect.5 This phenomenon causes an erratic estimation for the kinetic parameters. To specify the serious trouble that supposes the experimental error, Gronli et al.6 coordinated the realization of a round-robin kinetic study for the cellulose pyrolysis (Avicel PH-105) in eight European laboratories. Results confirmed the theories of Antal and coworkers. Additionally, Gronli and co-workers alerted the scientific community about the convenience of carrying out this experiment (under standard conditions) in order to be able to quantify their own experimental errors. Once the thermal degradation of the cellulose was characterized and the causes that originate contradictory results were analyzed, it was necessary to specify a model to describe the primary pyrolysis of the lignocellulosic materials. Indeed, many authors7-9 consider that general biomass pyrolysis behaves as a superposition of the independent kinetics of the primary components (hemicellulose, cellulose, and lignin). Consequently, the global production of volatile matter corresponds to the summation of the individual contributions from the three natural polymers. Recently, Teng and Wei10 assumed this summative hypothesis to characterize the rice hulls pyrolysis. They estimated the kinetic parameters for each component (activation energy, preexponential factor, and volatile matter final weight) using a nonlinear least-squares algorithm (NLLS) applied to the differential weight loss curve (DTG). These authors assumed that each polymer decomposes according to an irreversible single-step firstorder reaction. It has been shown that a three-step model with firstorder rates provides a good fit of the experimental measurements (several thermogravimetric studies of
10.1021/ie020218p CCC: $25.00 © 2003 American Chemical Society Published on Web 12/19/2002
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diverse biomass, restricted to the low heating rate, have been reported) when the first step (mainly hemicellulose decomposition) is described by activation energies in the range 80-908,11 or 105-111 kJ/mol.12 However, Teng and Wei have reported a higher activation energy value for the first step (154-165 kJ/mol). The published data concerning the third reaction step (mainly lignin decomposition) present higher differences: range 35-65,12 18-20,8 or 34-36 kJ/mol.10 On the other hand, sugarcane bagasse has been pyrolyzed at low heating rates in several thermogravimetric studies.13-15 In these contributions and over the interval 200-400 °C, attention was focused upon the decomposition of the cellulose fraction of bagasse, with lignin decomposition rates being significant over the wider temperature range of 150-750 °C.16 Kinetic analysis applied to bagasse based upon an independentparallel-reactions model, developed using best-fit criteria, and applied to these studies yields activation energies of typically 226 kJ/mol,2 which are significantly greater than those obtained at high heating rates, typically 59.517 or 92.6 kJ/mol.18 These conflicting kinetic data for bagasse pyrolysis mirror the discrepancies reported for the activation energies of cellulose pyrolysis under slow and fast heating regimes. One explanation for this discrepancy is the severe impact of heat-transfer limitations on kinetic studies of endothermic reactions at high temperatures (where the rate of reaction and its consequent heat demand are large). A consequence of heat-transfer limitations is that the single-step activation energy measured at high heating rates is almost always lower than its true value.2 The objectives of this study are to determine the kinetic parameters of the global mass loss during slow pyrolysis of two types of biomass (waste wood and sugarcane bagasse) and to simulate the pyrolytic behaviors using the determined parameters. The thermal characteristics of both untreated and water-washed biomass have additionally been explored in the present work. It has been reported that the presence of inorganic impurities can dramatically affect the course of cellulose pyrolysis.2 Experiments have been carried out in a thermobalance (at low heating rates). The experimental results have been subjected to a process of kinetic evaluation adopting, at the beginning, the methodology suggested by Teng and Wei. The thermogravimetric apparatus error has been analyzed prior to the realization of experiments, to ensure the validity of the experimental data.
Table 1. Biomass Sample Analysis proximate (% by wt) biomass
moisture
volatile matter
fixed carbon
ash
untreated waste wood washed waste wood untreated bagasse washed bagasse
8.2 5.7 2.2 4.0
64.1 70.1 75.0 79.3
23.4 21.7 17.8 15.5
4.3 2.5 5.0 1.2
ultimate (% by wt; dry ash-free samples) biomass
C
H
N
O
untreated waste wood washed waste wood untreated bagasse washed bagasse
45.7 45.7 43.6 44.5
5.8 5.6 5.5 5.7
0.4 0.2 0.3 0.2
48.1 48.5 50.6 49.6
To analyze the influence of their inorganic matter content, both biomass samples were washed with hot water at 80 °C during 2 h, according to the procedure used by Teng and Wei10 and also suggested by other authors.2 The experimental apparatus was a CAHN TG-151 TGA equipment (vertical balance, 10 µg resolution, 100 g maximum sample weight, 1100 °C maximum temperature at atmospheric pressure, 70 bar maximum pressure at 1000 °C, and 25 K/min maximum heating rate). Experimental runs were carried out at three different heating rates (5, 10, and 20 K/min) under atmospheric pressure, with a sample weight of 10 mg and with a nitrogen purge flow (to remove vapor pyrolysis products) of 200 mL/min. The final temperature corresponded to 900 °C. The crucible was open at the top and made of platinum mesh with the purpose of minimizing the mass-transfer limitations. Experimental Test with Pure Cellulose Following Gronli et al.6 recommendations, a thermogravimetric run with chemically pure cellulose (AVICEL PH-101 supplied by Sigma-Aldrich) was realized to compare the experimental results with the data published in the above-mentioned paper. The experiment was carried out using 4 mg as the initial weight, at 5 K/min heating rate and with a nitrogen purge flow of 200 mL/min. Although the TGA apparatus used in this work has a lower resolution than the instruments used in the comparative study (0.1-1.25 µg), results showed a good agreement. Experimental data were used to fit the kinetic model proposed by Antal and co-workers (eq 1),
dR ) K(1 - R) dt
Experimental Section In this section, the characteristics of the TGA equipment used in this work and the experimental conditions are described. The biomasses under study were as follows: sugarcane bagasse from the region of Tucuma´n [Argentina; the samples were taken from a homogenized mixture of different bagasse variants (with different shaft proportions)] and waste wood from Barcelona’s (Spain) public parks and gardens cleaning, which is classified as softwood.19 The selected fraction of particle size was in the range of -1.2 to +0.25 mm in both cases. The proximate analysis and elemental composition of the biomass samples used in this study are shown in Table 1.
(1)
where
R)1-
m(t) - mf m0 - mf
and
K ) A exp(-E/RT)
m(t) is the experimental weight at each monitoring time, mf is the final weight, and m0 is the initial dry mass (after weight stabilization at 110 °C). K is the kinetic constant, which, according to the Arrhenius equation, is a function of the preexponential factor (A), the apparent activation energy (E), the absolute temperature, and the constant of the ideal gas law (R).
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Table 2. Kinetic Evaluation of Avicel Cellulose at 5 K/min
experimental (Cahn TG-151)
mean value of kinetic parameters from round-robin study (Gronli et al.6)
standard deviation of kinetic parameters from round-robin study (Gronli et al.6)
243.3 18.0 0.900 338 1.1
244.0 19.0 0.916 327 0.6
10.0 1.1 0.024 5 0.2
E (kJ mol-1) log A (s-1) 1 - mf/m0 Tpeak (°C) % fit (simulated curve)
From the DTG experimental curve, points near the maximum value (Tpeak) were considered, and using a nonlinear least-squares algorithm (NLREG version 3.3), the kinetic parameters (A, E, and mf) were estimated. NLREG20 is a statistical analysis program that performs nonlinear regression analysis and curve fitting. The program determines, for a defined function and a set of initial values, the values of parameters that cause an equation to best fit a set of data values. The goal of regression analysis is to minimize the squared residuals for the actual function (eq 1 without linear transformation) and the estimated values of the parameters. The objective function STG is as follows. N
STG )
2 [(m(t))jexp - (m(t))calc ∑ j ] j)1
(2)
In eq 2, (m(t))exp is the experimentally observed TG mass measurement, and (m(t))calc is the calculated mass value obtained by numerical solution of the first-order rate equation with the given set of parameters. N is the number of evaluated data points, and subscript j denotes discrete values of m(t). The average values obtained in the study of Gronli and co-workers have been adopted as starting values for the fitting procedure. The calculated parameters were used to simulate the weight loss curve. A good fit to the experimental data points was obtained. Table 2 compares the parameters obtained with the average values published by Gronli and co-workers. The error percentage, from the adjustment of the experimental data points, can be calculated from the following expression:
fit (%) )
100xSTG/N m0
(3)
Results show a good agreement with the data obtained by other laboratories, except for the Tpeak value (see Table 2). The higher value of the peak temperature may explain the lower value obtained in this work for the preexponential factor. The differences in temperature measurement should be attributed to systematic error of the thermogravimetric instrument. However, the kinetic data estimated in the present study, for the pyrolysis of Avicel cellulose, are in agreement with the findings from the round-robin study. Consequently, we considered reasonable the impact of systematic errors on the interpretation of data obtained from the Cahn TG-151 apparatus. Kinetic Study of Biomass Pyrolysis The present section describes the methodology adopted for the parameters estimation for each single TG curve obtained at 5 K/min (the lowest heating rate).
Figure 1. Comparison between the volatile evolution rate curves for the untreated (dashed line) and for the water-washed (solid line) sugarcane bagasse (β ) 5 K/min).
From the DTG experimental curve (see an example in Figure 1), the points near the first peak are considered (this peak corresponds to the maximum decomposition rate for the hemicellulose). These values are correlated using a first-order kinetic model based on eq 1 but expressed as a function of the volatile matter accumulated production (V),
dV -E ) A exp (V* - V) dt RT
[ ]
(4)
where the V* parameter is the final quantity of volatile matter generated from the hemicellulose pyrolysis. Using the same procedure followed in the above section, parameters A, E, and V* are estimated to simulate, later on, the weight loss curve due exclusively to the hemicellulose devolatilization. At this point, the following questions arise: do the experimental weight losses experienced only correspond to the hemicellulose decomposition? Is there overlapping with the lignin devolatilization? According to different authors,13,16 the lignin decomposes over a wide temperature range and, subsequently, it is very probable that the volatile matter generation from hemicellulose has been overestimated. To amend this error, the following procedure to adjust kinetic parameters associated with the hemicellulose has been envisaged: The maximum value of the simulated DTG curve (for the hemicellulose devolatilization) is compared with the experimental DTG value (at the same temperature value). The ratio between these two values is calculated. This correction factor is associated with the initial estimation of V*. Finally, the hemicellulose weight loss is simulated with the new value of V*. For the estimation of the kinetic parameters associated with the cellulose decomposition, the points near the second peak are considered. From experimental volatile matter accumulated production values (m(t) m0), the accumulated production of volatile matter associated with the hemicellulose decomposition (obtained by simulation) are subtracted. With a kinetic model like that corresponding to eq 4, parameters A,
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Figure 2. Kinetic evaluation of the Kraft alkali lignin DTG curve using the best-fit parameters for each model.
E, and V* are considered. This last one has been subjected to a process fit under the same approach as that used with the previous polymer. Finally, for the lignin, experimental data points which were sufficiently far away from the maximum peak (+80 °C) have been considered. The volatile matter accumulated production values corresponding to these points are attenuated by the simulated productions of volatile matter associated with the carbohydrates. Initially, a first-order kinetic model has been considered for the lignin pyrolysis. To determine the best-fit kinetic parameters for each pseudocomponent (Ai and Ei), the values reported by Teng and Wei10 have been considered as starting parameter values. From the kinetic parameters associated with each natural polymer, the weight loss curves are simulated following Teng and Wei’s model by means of the following expression:
dV
3
)
dt
Ai exp ∑ i)1
( ) -Ei RT
(V*i - Vi)
Figure 3. Pyrolysis (at 5 K/min) of the untreated (a) and waterwashed (b) sugarcane bagasse. Simultaneous kinetic evaluation of the TG curves assuming the initial model shown in eq 5 [sim(1)] and the new proposed model shown in eq 10 [sim(3)]. The kinetic parameters used to create the calculated curves are listed in Tables 3 and 4.
(5)
In fact, and according to Orfao et al.,8 the subscripts i do not refer exactly to the natural polymers that integrate the biomass but to the three following pseudocomponents: (a) i ) 1. A hemicellulose fraction that decomposes in the low-temperature range (first peak of DTG curve). (b) i ) 2. A cellulose fraction that decomposes in the low-temperature range (second peak of DTG curve). (c) i ) 3. The fraction of lignin present plus extractives and the remaining amounts of holocellulose. Initial Results Results, obtained under the operational methodology reported in the above section, have not been satisfactory enough. The agreement between the experimental TG curves and the model predictions from eq 5 has been shown to be poor (see Figures3 and 4). Moreover, as seen in Table 3, there is a substantial dispersion degree for the third pseudocomponent kinetic parameters. Kinetic Study with Pure Lignin In light of the reduced reliability of the previous results, a thermogravimetric study for the pure lignin thermal decomposition (present in the third pseudocomponent) was carried out. In contrast to cellulose (Avicel PH-101, PH-105; Whatman CF11), a lignin standard product does not exist. Drummond and Drummond18 use Kraft lignin and Ouensanga and Picard13 Klason lignin
Figure 4. Pyrolysis (at 5 K/min) of the untreated (a) and waterwashed (b) waste wood. Simultaneous kinetic evaluation of TG curves assuming the initial model shown in eq 5 [sim(1)] and the new proposed model shown in eq 10 [sim(3)]. The kinetic parameters used to create the calculated curves are listed in Tables 3 and 4.
(obtained by digestion of biomass samples with mineral acids). In this work, the commercial product “Kraft alkali lignin” (supplied by Sigma-Aldrich; catalog no. 37,095-9) has been used. This product is obtained by acidification of an alkaline extract from wood. The experimental conditions have been the follow-
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Table 3. Kinetic Evaluation of TG Curves (Obtained at a Heating Rate of 5 K/min) Assuming the Initial Model Shown in Eq 5 first pseudocomponent (hemicellulose) untreated bagasse water-washed bagasse untreated waste wood water-washed waste wood
V*/m0
A (s-1)
0.126 0.169 0.119 0.108
4.7 × 4.7 × 1015 4.7 × 1015 4.7 × 1015 1015
second pseudocomponent (cellulose)
E (kJ mol-1) 194.0 200.0 196.9 199.5
ing: sample weight, 10 mg; heating rate, 5 K/min; final temperature, 900 °C; purge flow, 200 mL/min (nitrogen); pressure, 1 bar; crucible, open (platinum mesh). For the evaluation of the Kraft lignin kinetic parameters, the classic TPDE methodology was adopted. The analysis of the experimental data points was carried out using the following expression:
ln
(
) ()
E dR A ) ln β RT dTf(R)
(6)
where β is the heating rate and f(R) is a nonisothermal continuous function. According to Conesa et al.,21 a model based on “n-order of reaction” was adopted:
f(R) ) (1 - R)n
(7)
The obtained experimental data points for a temperature range between 225 and 675 °C were adjusted using the linear equation (6), assuming 0.5, 1, 2, and 3 for the reaction order, which correspond respectively to the mechanisms R2, G1, S, and T enunciated by Sesta´k.22 The best fitting (linear regression coefficient equal to 0.9889) was obtained for the T mechanism, giving 52.2 kJ/mol for the activation energy and 20 s-1 for the preexponential factor. Adopting this kinetic law, the reformulated model for the primary lignin pyrolysis can be written as
dR -E (1 - R)3 ) A exp dt RT
( )
(8)
or 3 -E (V* - V) dV ) A exp dt RT V*2
( )
(9)
The best-fit kinetic parameters for the third-order kinetic model were estimated using the NLREG program. The values obtained from the TPDE analysis have been considered as starting values for the fitting procedure. As for the initial value of V*, we have adopted the experimental mass value obtained at 900 °C. To determine the best-fit kinetic parameters for the first-order kinetic model, the same procedure has been followed. In this case, the starting values of the Arrhenius parameters coincide with those reported by Teng and Wei for the lignin present in rice hulls. The volatile yields during pyrolysis in the TGA are compared with the DTG simulated curves obtained from the two models, using the best-fit parameters (see Figure 2). The same figure shows that when a thirdorder reaction is introduced in the model, it is possible to obtain better fits. Evaluation of the Proposed Summative Model Experimental results obtained at a heating rate of 5 K/min for the thermal decomposition of biomass were
third pseudocomponent (lignin)
V*/m0
A (s-1)
E (kJ mol-1)
V*/m0
A (s-1)
E (kJ mol-1)
0.392 0.449 0.295 0.279
1.0 × 1.0 × 1018 1.0 × 1018 1.0 × 1018
243.3 249.6 245.7 250.8
0.220 0.186 0.275 0.304
0.7 69.2 10.1 1.1
37.0 62.5 51.4 40.2
1018
Table 4. Kinetic Parameters for the Third Pseudocomponent Decomposition Assuming the Third-Order Kinetic Modela
untreated bagasse water-washed bagasse untreated waste wood water-washed waste wood
log A3 (s-1)
E3 (kJ mol-1)
V*3/m0
h (%)
fit (%)
%g
1.9
53.6
0.254
25.1
1.08
53.6
2.3
58.2
0.219
17.2
0.89
68.9
2.1
57.5
0.332
27.2
0.69
80.1
2.3
60.9
0.341
26.0
1.69
52.7
a Influence of the new kinetic evaluation on simulated biomass pyrolysis TG curves obtained at 5 K/min.
analyzed using the summative model from eq 5. In this case, the term corresponding to the third pseudocomponent, which was assumed initially to follow a firstorder kinetic law, was replaced by the expression given by eq 9. The new mathematical expression is as follows:
( )
-E1 dV (V*1 - V1) + ) A1 exp dt RT A2 exp
( )
-E2 (V*2 - V2) + RT
( )(
)
-E3 (V*3 - V3)3 (10) A3 exp RT V*32
where the subscripts 1-3 represent the three different pseudocomponents of the biomass. Table 4 summarizes the best-fit kinetic parameters obtained using eq 10 for the devolatilization of the third pseudocomponent (kinetic parameters for both the first and second pseudocomponents gave the same results as those shown in Table 3). In this case, the starting values of the Arrhenius parameters for the third pseudocomponent coincide with those determined in the previous study with Kraft alkali lignin (A ) 407.7 s-1; E ) 65.4 kJ/mol). Table 4 also reports the experimental char yields [η ) 100(mlast/m0)], the error of the TG simulated curves according to the expression (3), and the reduction, with the proposed model, of the fit error (% g). Figures 3 and 4 illustrate the better efficiency of the new kinetic model (for experiments at 5 K/min). To determine a same set of kinetic parameters suitable for simulating the decomposition reaction carried out at all heating rates (5, 10, and 20 K/min), a procedure followed by previous workers4,10 was adopted in the present study. The application of this procedure was principally based on the assumption that the effects of thermal lag on pyrolysis kinetics can be described by incorporating the uncertainty associated with the temperature measurement into an uncertainty in the value of log A. As discussed in earlier papers,4,6,23 this approach has merit because small changes in the value of
Ind. Eng. Chem. Res., Vol. 42, No. 3, 2003 439 Table 5. Kinetic Parameters of the Proposed Summative Model pseudocomponent first
second
third
log Ai (s-1) Ei (kJ/mol) V*i/m0
Untreated Bagasse 15.5 ( 0.1 17.9 ( 0.1 194.0 243.3 0.13 0.41
2.0 ( 0.2 53.6 0.22
log Ai (s-1) Ei (kJ/mol) V*i/m0
Water-Washed Bagasse 15.6 ( 0.1 17.9 ( 0.1 200.0 249.6 0.18 0.44
2.4 ( 0.2 58.2 0.22
log Ai (s-1) Ei (kJ/mol) V*i/m0
Waste Wood 15.7 ( 0.1 18.0 ( 0.1 196.9 245.7 0.13 0.30
2.3 ( 0.4 57.5 0.31
log Ai (s-1) Ei (kJ/mol) V*i/m0
Water-Washed Waste Wood 15.7 ( 0.1 18.1 ( 0.1 199.5 250.8 0.13 0.30
2.5 ( 0.1 60.9 0.31
log A (at constant E) result in temperature shifts of the weight loss curve without significant changes in the actual shape of the curve. In this way, the uncertainty associated with the temperature measurement is represented as an uncertainty in the value of log Ai. Following this procedure, we fixed the value of the parameter Ei at the value determined at 5 K/min (see Tables 3 and 4) and calculated new values of log Ai to give a good fit to each individual TG curve. As for the values of the ultimate volatile yield for each pseudocomponent (V*i), we considered for each biomass sample the mean value of those determined from different heating rates. The resulting kinetic parameters are listed in Table 5, showing a single value of Ei and values of log Ai ( δ log Ai, where δ log Ai is the standard deviation of the values of log Ai employed to fit the data for different heating rates. The weight losses during pyrolysis in the TGA are compared with the simulated curves obtained from the proposed summative kinetic model, using eq 10 combined with the kinetic parameters in Table 5. The model predictions and the experimental data are displayed in Figures 5 and 6, showing that this model successfully predicts the global mass loss process of the pyrolysis for the samples used in this work. On the other hand, Figures 5 and 6 show that the experiments have a certain degree of variance, difficult to avoid in practice. This fact can be related to the variability of the initial sample (10 mg) composition. This variability would explain the unexpected behavior shown in Figure 6a in which the 20 K/min line crosses the 10 K/min line. Additionally, the following remarks can be made from the results reported in Tables 3-5: (1) The estimated kinetic parameters for the second pseudocomponent are the same as those obtained for the pure cellulose test (see Table 2). For the energy of activation, a maximum 2.8% deviation with respect to the 244 kJ/mol reference value is obtained (250.8 kJ/ mol for the washed waste wood). Indeed, according to Antal and Varhegyi,2 the degree of polymerization and crystallinity of cellulose does not affect significantly the pyrolysis kinetics. (2) For the hemicellulose (first pseudocomponent), the obtained parameters are still higher than those published by Teng and Wei.10 However, this discrepancy can be justified from the possible interference of the lignin decomposition. Approximating several indepen-
Figure 5. TG curves during pyrolysis of the untreated (a) and water-washed (b) sugarcane bagasse. The symbols are experimental data ([, 5 K/min; 2, 10 K/min; ×, 20 K/min); the solid line curves are predictions from the proposed summative model using parameters shown in Table 5.
dent parallel reactions by a single reaction may lead energies of activation and preexponential factors much lower than the true values. (3) The estimated values for the energy of activation for the third pseudocomponent (mainly composed of lignin) oscillate in the range of 49.6-60.9 kJ/mol. This result can be analyzed from two points of view, their dispersion and their absolute values. The degree of dispersion is much lower than that obtained using the initial model (see Table 3). On the other hand, the absolute values of the E parameter are much higher than those reported by, for example, Teng and Wei10 (approximately 35 kJ/mol) and by Orfao et al.8 (18-20 kJ/mol) determined from first-order rates. However, the third pseudocomponent is a mixture of substances and, consequently, the estimation of the kinetic parameters is not free from uncertainty. (4) Finally, from the analysis of the experimental char yield results, the following remarks can be drawn: (a) The higher char yields obtained from the wastewood samples compared to those from sugarcane bagasse can be attributed to its higher lignin content, which is the main promoter of char. (b) A significant reduction of the char yields was observed for the washed sugarcane bagasse. This can be attributed to the inorganic matter effect (watersoluble) during the pyrolysis process. Despite the promotion of the devolatilization at the low-temperature range (catalytic effect), the mineral matter promotes the formation of char. This experimental result corroborates the selective action that plays the inorganic matter in the cellulose decomposition (reducing the levoglucosan yield, which decreases the liquid fraction formation in benefit of char and gases2). Figure 1 shows a comparison
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of the feedstocks can justify the degree of variability in their composition. However, the methodology developed in this study can be applied to the kinetic characterization for a variety of biomass samples. In this sense, further investigations are in progress to verify the ability of the proposed model to describe the global mass loss during pyrolysis of additional biomass samples (pine and beech wood). Otherwise, the experimental results have corroborated the influence of the inorganic matter present in the biomasses. For the sugarcane bagasse, the presence of mineral matter promotes the char formation as detrimental to the volatile condensable fraction. Finally, the realization of thermogravimetric tests with pure cellulose (Avicel PH-101) has validated the results, which were in good agreement with those obtained by other European laboratories. These results guarantee the reliability of the experimental apparatus used in this study. Acknowledgment This work was financed in part by the Commission of the European Union (Project No. 7220-ED-081). J.J.M. acknowledges the scholarship grant received from the Departament d’Universitats, Recerca i Societat de la Informacio´ de la Generalitat de Catalunya. Figure 6. TG curves during pyrolysis of the untreated (a) and water-washed (b) waste wood. The symbols are experimental data ([, 5 K/min; 2, 10 K/min; ×, 20 K/min); the solid line curves are predictions from the proposed summative model using parameters shown in Table 5.
between DTG experimental curves for untreated and washed sugarcane bagasse under the same conditions. (c) For the waste wood, the influence of the inorganic matter has been shown to be less significant. The differences in the qualitative and quantitative composition of inorganic matter present in both biomasses under study can justify this result. Conclusions In this work, a modified summative kinetic model for biomass pyrolysis, based on the analysis of dynamic thermogravimetric curves obtained under kinetic control, is proposed. The reformulated model assumes the noninteraction hypothesis in the thermal decomposition of the natural polymers (hemicellulose, cellulose, and lignin). However, this study represents an advancement with respect to the current state of the art: the characterization of the Kraft alkali lignin pyrolysis by means of a third-order kinetic model. Predictions from the model proposed here (contemplating three pseudocomponents) reproduce correctly the experimental TG curves during the pyrolyisis of the two lignocellulosic materials analyzed in this study (sugarcane bagasse and waste wood). The model has been coupled with the following methodology for kinetic evaluations: the possible interference of the lignin devolatilization with homologous processes of the other polymers has been corrected using a correction factor deduced from the comparison between DTG curves. The authors are aware of the difficulty of characterizing the samples studied to be able to reproduce these experiments in other laboratories. The residual origin
Nomenclature A ) preexponential factor (s-1) DTG ) differential weight loss curve (mg s-1) E ) energy of activation (J mol-1) K ) kinetic constant m(t) ) experimental sample mass (mg) m0 ) initial dry sample mass, defined as m(t) at 110 °C (mg) mf ) final weight parameter (mg) mlast ) experimental final sample mass at 900 °C (mg) n ) reaction order R ) gas constant (J mol-1 K-1) STG ) sum of the quadratic deviations (between the experimental values and the simulated ones from the kinetic model) t ) time (s) T ) temperature (K) TG ) thermogravimetry TGA ) thermogravimetric analyzer TPDE ) temperature-programmed decomposition V ) accumulated production of volatile matter (mg) V* ) final accumulated production of volatile matter (mg) Greek Symbols R ) reacted fraction β ) heating rate (K min-1) η ) experimental char yield
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Resubmitted for review July 22, 2002 Revised manuscript received November 14, 2002 Accepted November 18, 2002 IE020218P
