Article pubs.acs.org/EF
Effective Activation Energies of Lignocellulosic Biomass Pyrolysis Weixuan Wu, Yuanfei Mei, Le Zhang, Ronghou Liu, and Junmeng Cai* Biomass Energy Engineering Research Center, Key Laboratory of Urban Agricultural (South) Ministry of Agriculture, School of Agriculture and Biology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, People’s Republic of China ABSTRACT: Eight different three-parallel distributed activation energy model (DAEM) reaction model processes, which were used to describe the pyrolysis of eight lignocellulosic biomass samples very well, were analyzed by means of the Cai−Chen iterative linear integral isoconversional method. The activation energies obtained from the isoconversional method were independent of the heating rate, which indicated that the isoconversional analysis was valid for the pyrolysis of lignocellulosic biomass. The resulting effective activation energies of the pyrolysis of all lignocellulosic biomass samples showed strong dependence upon the extent of conversion: in the low range of conversion, the effective activation energies increase (about 190− 210 kJ mol−1) with increasing the extent of conversion; in the medium range of conversion, the effective activation energies exhibit a practically constant value (about 210 kJ mol−1); and in the high range of conversion, the effective activation energies increase (about 210−290 kJ mol−1) with increasing the extent of conversion.
1. INTRODUCTION Lignocellulosic biomass, including the forms of virgin biomass (trees, bushes, grass, etc.), waste biomass (crop residues, agroindustrial residues, forest residues, etc.), and energy crop (sugar cane, sweet sorghum, etc.), can be converted into biofuels and chemicals using thermochemical or biochemical conversion technologies.1,2 Lignocellulosic biomass is composed of varying amounts of cellulose, hemicellulose, and lignin. Cellulose is a polysaccharide of the general formula (C6H10O5)n (where “n” is the degree of polymerization), which usually consists of 5000− 10 000 glucose units.3 Unlike cellulose, hemicellulose is a heteropolysaccharide composed of various carbohydrate monomers with different linkages and substitutions on the primary branches.4 The most common hemicellulose is xylan, which has a backbone of β-1,4-linked xylopyranose units. Lignin is a macromolecule, which has a complex three-dimensional structure and consists of three major phenylpropanoid units: p-coumaryl, coniferyl, and sinapyl alcohols.5,6 The chemical structures of cellulose, xylan (a representative hemicellulose), and three major phenylpropanoid units of lignin are shown in Figure 1. Pyrolysis is a thermochemical decomposition of lignocellulosic biomass at certain temperatures in the absence of oxygen.7 The discrepancy of biomass pyrolysis behavior is attributed to the differences in the inherent structures and chemical nature of its components, which decompose at different rates and through different mechanisms and pathways.8,9 Hemicellulose is easy to decompose at low temperatures.10 Cellulose pyrolysis follows a higher temperature range.11 The activity of the chemical bonds in lignin covers an extremely wide range, which leads to the degradation of lignin occurring in a wider temperature range from ambient up to 1170 K.8 There are many kinetic models to describe the pyrolysis of cellulose, hemicellulose, and lignin. These models range from single-step kinetics to parallel and consecutive reaction schemes.12 In these models, the distributed activation energy model (DAEM) involves the distribution of activation energies reflecting variations in the bond breakage of species in those biomass components.13,14 Many researchers have reported the © 2014 American Chemical Society
Figure 1. Chemical structures of (a) cellulose, (b) xylan (a representative hemicellulose), and (c) three major phenylpropanoid units of lignin: (i) p-coumaryl alcohol, (ii) coniferyl alcohol, and (iii) sinapyl alcohol.
model consisting of multiple DAEM reactions as the best kinetic model to describe the pyrolysis of lignocellulosic biomass. In this type model, the pyrolysis of lignocellulosic biomass was considered to be the sum of the pyrolysis of several pseudocomponents. These pseudo-components are linked to cellulose, hemicellulose, and lignin, and there are no interactions among them. Várhegyi et al.15 regarded biomass as the mixture of two pseudo-components, in which one is cellulose and the other Received: March 17, 2014 Revised: May 5, 2014 Published: May 6, 2014 3916
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Table 1. Kinetic Parameters of Lignocellulosic Biomass Pyrolysisa
a
parameter
corn stover
cotton stalk
palm oil husk
pine wood
red oak
sugar cane bagasse
switch grass
wheat straw
E01 (kJ mol−1) E02 (kJ mol−1) E03 (kJ mol−1) σ1 (kJ mol−1) σ2 (kJ mol−1) σ3 (kJ mol−1) A1 (s−1) A2 (s−1) A3 (s−1) c1 c2 c3
179.602 207.381 239.344 5.888 1.174 31.414 1013.007 1013.949 1016.003 0.16337 0.25386 0.16481
178.188 205.287 239.463 5.591 1.789 41.767 1013.132 1013.800 1015.738 0.12999 0.35824 0.20008
169.710 199.966 236.110 5.147 1.313 39.998 1012.699 1013.618 1015.953 0.10116 0.32121 0.19241
186.701 214.364 271.758 8.769 1.126 29.250 1013.006 1013.803 1016.399 0.32147 0.38557 0.08412
183.109 209.58 242.145 8.342 0.706 26.583 1013.007 1013.618 1015.006 0.27282 0.37320 0.12204
184.750 212.483 234.759 5.419 1.339 36.493 1013.007 1013.805 1015.769 0.22009 0.35035 0.12289
186.776 212.057 260.952 4.586 1.361 39.293 1013.007 1013.804 1016.536 0.23651 0.33376 0.20159
175.506 204.244 240.610 5.375 1.130 38.422 1012.700 1013.800 1015.812 0.15916 0.28027 0.23410
The kinetic parameters were adopted from the literature.17 farm in Massachusetts, U.S.A., and cotton stalk and wheat straw from a farm in Jiangxi province, China. The properties of the samples can be found in the literature.17 Because the model involving three-parallel DAEM reactions has proven very accurate in describing the pyrolysis of various biomass feedstocks at different heating rates,17 the three-parallel DAEM reaction model processes for the pyrolysis of those lignocellulosic feedstocks were analyzed. In the model, the pyrolysis of lignocellulosic biomass was regarded as a sum of the decomposition of three pseudocomponents, which were linked to hemicellulose, cellulose, and lignin, and the decomposition of each pseudo-component can be described by a single first-order DAEM.17 The DAEM assumed that a large number of parallel reactions with different activation energies occur during the pyrolysis of complex organic materials, and the difference in activation energies can be represented by the Gaussian distribution.22 According to the above assumptions, the conversion and its rate for the pyrolysis of lignocellulosic biomass can be expressed by the following equations:
consists of hemicellulose, lignin, and extractives. They employed the two-parallel DAEM reaction model to analyze the pyrolysis of wheat, oat, barley, and Brassica carinata straws and found that the model described the experimental data very well. Várhegyi et al.16 developed a model consisting of three parallel DAEM reactions to describe the pyrolysis of corn stalk, rice husk, sorghum straw, and wheat straw. In their study, those DAEMs were used to analyze the decomposition of cellulose, hemicellulose, and lignin. Cai et al.17 reported that the major portion of cellulose and hemicellulose reacts in a relatively narrow range of low temperatures and that there is a residual amount that volatilizes almost to the end of the pyrolysis of cellulose and hemicellulose. They also proposed a three-parallel DAEM reaction model and used it to describe the pyrolysis of eight lignocellulosic materials successfully.17 In their model, the first and second pseudo-components represent the fractions of hemicellulose and cellulose that react in a relatively low range of temperatures, and the third pseudo-component represents the sum of the remaining amounts of these components plus the fractions of lignin.17 The isoconversional methods are commonly used to obtain the apparent activation energies of complex solid-state processes. However, the isoconversional methods are not valid when they are used to analyze the parallel, competitive, and successive reactions, and therefore, the obtained activation energies of those reactions have no physical meaning.18,19 Wu et al.20 employed the Friedman isoconversional method to analyze seven different single DAEM processes and found that the isoconversional method is valid for the single DAEM process. They concluded that the single DAEM process is equivalent to the real varying activation energy process. However, the isoconversional kinetic analysis of the three-parallel DAEM reaction processes for the pyrolysis of lignocellulosic biomass is still missing in the literature. This is the first aim of this paper. In the computational fluid dynamics modeling of biomass pyrolysis for describing practical conversion processes and designing more efficient reactors, the effective activation energies are required.21 Therefore, our second aim is to calculate the effective activation energies of the complex processes involved in three-parallel DAEM reactions for lignocellulosic biomass pyrolysis.
3
α=
∑ j = 1 cjαj(T ) 3
∑ j = 1 cj
(1)
dαj(T )
3
∑ j = 1 cj dT dα = 3 dT ∑ j = 1 cj
αj = 1 −
1 2π σj
(2)
∫0
∞
⎡ exp⎢ − ⎢⎣
∫0
T
(E − E0, j)2 ⎤ ⎛ E ⎞ ⎥dE ⎟dT − exp⎜ − ⎝ RT ⎠ ⎥⎦ β 2σj 2
Aj
(3)
dαj dT
1 2π σj
=
−
∫0
∞
⎡ E exp⎢− − ⎢⎣ RT β
Aj
(E − E0, j)2 ⎤ ⎥dE ⎥⎦ 2σj 2
∫0
T
Aj β
⎛ E ⎞ ⎟dT exp⎜ − ⎝ RT ⎠
(4)
In the above equations, α is the extent of conversion, R is the universal gas constant, T is the temperature, E is the activation energy, the subscript j represents the values related to the jth pseudo-component, E0 and σ are the mean value and standard deviation of the activation energy distribution, respectively, A is the frequency factor, and cj is the amount of volatiles formed from the jth pseudo-component. The model parameter values for those lignocellulosic biomass samples were taken from our previous paper17 and listed in Table 1. On the basis of eqs 1−4, the conversion and conversion rate data for the pyrolysis of those lignocellulosic biomass samples were obtained by means of numerical integration.
2. MATERIALS AND MODEL The lignocellulosic biomass samples under study were the same feedstocks presented in our previous work:17 corn stover, palm oil husk, pine wood, red oak, sugar cane bagasse, and switch grass from a 3917
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Because of the fact that the left-hand side of eq 10 is the function of the unknown parameter, Eα−Δα/2, Cai and Chen28 gave an iterative procedure to calculate Eα−Δα/2. In this work, the Cai−Chen isoconversional method would be used for the latter kinetic analyses of lignocellulosic biomass pyrolysis.
3. ISOCONVERSIONAL METHODS The isoconversional methods were found to be suitable for the kinetic analysis of complex thermally activated solid-state reactions via a variation of the activation energy with the conversion.23 The differential isoconversional method by Friedman can provide the accurate values of activation energies compared to other isoconversional methods. However, the use of the Friedman method requires the conversion rate data, which would lead to be numerically unstable and noisesensitive.24 The popular linear integral isoconversional methods [including the Ozawa−Flynn−Wall (OFW) and Kissinger−Akahira− Sunose (KAS) methods] were originally derived with a constant activation energy and use some oversimplified temperature integral approximations for linearization.25 Thus, these methods may lead to some systematic errors involved in the activation energy.18 The advanced nonlinear isoconversional method proposed by Vyazovkin26 allows for the activation energy to be accurately calculated, but its computational complexity is high because it involves some nonlinear optimization calculations.27 The above problems can be avoided using the iterative linear integral isoconversional method proposed by Cai and Chen.28 The Cai−Chen isoconversional method offers two major advantages over the above popular isoconversional methods. The first advantage is associated with performing integrations over small conversion and temperature segments that allow for eliminating the systematic errors occurring in the conventional linear integral isoconversional methods when the activation energy varies significantly with the conversion. Second, it is a linear method and allows the activation energy to be determined in much less time than the advanced Vyazovkin nonlinear method. The Cai−Chen method is based on the integration of the basic kinetic equation: α
g (α , α − Δα) =
∫α−Δα
Aα −Δα /2 dα = β f (α)
∫T
Tα
α −Δα
4. RESULTS AND DISCUSSION The use of the Cai−Chen isoconversional method requires the initial guess of Eα (designated as Eα(0)). Farjas and Roura29 stated that the Cai−Chen isoconversional method did not suggest any method to establish Eα(0). In fact, Eα can converge to the same value with an arbitrary positive value of Eα(0) after a sufficient number of iterations.30 To validate the above conclusion, the isoconversional analysis of the pyrolysis of corn stover was analyzed using the Cai−Chen method with significantly different values of Eα(0) (Eα(0) = 0.1, 1, 10, 100, and 1000 kJ mol−1). The iterative outputs at α = 0.1, 0.3, 0.5, 0.7, and 0.9 are listed in Table 2, where it can be seen that the convergence rate of the Cai−Chen isoconversional method very high, regardless of Eα(0), and the Eα values at different α converge to the precision of 0.001 kJ mol−1 within three or four iterations. On the basis of the α − T and dα/dT − T data, the Cai− Chen convergent isoconversional plots (ln{βiTα,i−2[h(xα,i) − xα,i2exα,ih(xα−Δα,i)/(xα−Δα,i2exα−Δα,i)]−1} versus −1000/RTα,i) at different α (α varies from 0.05 to 0.95 with a step of 0.05) for six heating rates (2.5, 5, 10, 20, 40, and 80 K min−1) were obtained and shown in Figure 2. For all pyrolysis processes considered in this work, the perfect linear relationship was obtained in the Cai−Chen convergent isoconversional plots for the whole set of heating rates considered. This indicates that the results obtained from the Cai−Chen isoconversional method are independent of the range of heating rates, and the isoconversional kinetic analysis is valid for the three-parallel DAEM reaction processes. Using the least-squares method, the value of Eα can be obtained on the basis of the Cai−Chen convergent isoconversional plot at the conversion α. In fact, Eα is the effective activation energy. Its concept was proposed by Vyazovkin31 and used for the glass transition in polystyrene, polyethylene terephthalate, and boron oxide32 and the pyrolysis of oil shale.33 It is different from the traditional activation energy, which is usually treated as a constant from the initial to final state of the reaction.34 The constant traditional activation energy is a very reasonable approximation for the single-step elementary reaction.31 However, the pyrolysis of lignocellulosic biomass occurs in multiple steps that have different reaction rates. In the pyrolysis process of lignocellulosic biomass, there is the diffusion effect of the pyrolysis volatile products on the reaction rate.31,35 The above effects are obviously inconsistent with the concept of the traditional activation energy. The isoconversional methods describe the complex solid-state reaction process kinetics using multiple single-step kinetic equations, each of which is associated with a certain degree of conversion.35 Therefore, the effective activation energy of the pyrolysis of lignocellulosic biomass is generally a composite value determined by the activation energies of various processes (including multiple reactions and diffusion process) as well as the relative contributions of these processes to the overall reaction rate.36 The obtained effective activation energies of the pyrolysis of eight lignocellulosic biomass samples at various conversions (from 0.05 to 0.95 with a step of 0.025) are shown in Figure 3. From this figure, it can be seen that the effective activation
⎛ Eα −Δα /2 ⎞ ⎟d T exp⎜− ⎝ RT ⎠
(5) where β is the heating rate, f(α) is the reaction model, and the subscript α − Δα/2 denotes the values related to a constant extent of conversion, α − Δα/2. Rearranging eq 5 yields
⎧ ⎫ ⎪ ⎪ β ⎬ ln⎨ 2 x α ⎤ xα e 2⎡ ⎪ ⎪ Tα ⎣⎢h(xα) − x 2e xα −Δα h(xα −Δα)⎦⎥ ⎪ ⎪ ⎩ ⎭ α −Δα ⎡ ⎤ Aα −Δα /2 R Eα −Δα /2 ⎥− = ln⎢ RTα ⎣ Eα −Δα /2g (α , α − Δα) ⎦
(6)
where xα =
Eα −Δα /2 RTα
xα −Δα =
(7)
Eα −Δα /2 RTα −Δα
h(x) = x 2e x
∫x
∞
(8)
e −x dx x2
(9)
For a series of non-isothermal experiments, i = 1, 2, ..., n, eq 6 becomes ⎧ ⎫ ⎪ ⎪ ⎪ ⎪ βi ⎬ ln⎨ 2 x α ,i ⎡ ⎤ x e α , i ⎪ T 2 h(x ) − h(xα −Δα , i)⎥ ⎪ ⎪ α , i ⎢⎣ α , i xα −Δα , i 2e xα −Δα , i ⎦⎪ ⎭ ⎩ ⎡ ⎤ Aα −Δα /2R E ⎥ − α −Δα /2 = ln⎢ RTα , i ⎣ Eα −Δα /2g (α , α − Δα) ⎦
(10) 3918
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a
−1
3919
(kJ (kJ (kJ (kJ (kJ (kJ (kJ
mol−1) mol−1) mol−1) mol−1) mol−1) mol−1) mol−1)
0.1 211.842 211.675 211.676 211.676 211.676 211.676
0.1 184.297 184.158 184.158 184.158 184.158 184.158
± ± ± ± ± ±
± ± ± ± ± ±
0.103 0.096 0.096 0.096 0.096 0.096
0.256 0.262 0.262 0.262 0.262 0.262
0.1 269.093 265.046 265.102 265.101 265.101 265.101
1 211.841 211.675 211.676 211.676 211.676 211.676
1 184.296 184.158 184.158 184.158 184.158 184.158
± ± ± ± ± ±
± ± ± ± ± ±
± ± ± ± ± ±
4.125 3.237 3.249 3.249 3.249 3.249
0.103 0.096 0.096 0.096 0.096 0.096
10 211.834 211.675 211.676 211.676 211.676 211.676 ± ± ± ± ± ±
10 0.256 184.289 ± 0.262 184.158 ± 0.262 184.158 ± 0.262 184.158 ± 0.262 184.158 ± 0.262 184.158 ± iterative output 0.103 0.096 0.096 0.096 0.096 0.096 1 269.079 265.046 265.102 265.101 265.101 265.101
100 211.763 211.675 211.676 211.676 211.676 211.676
100 0.256 184.220 0.262 184.158 0.262 184.158 0.262 184.158 0.262 184.158 0.262 184.158 at α = 0.5
iterative output at α = 0.1
± ± ± ± ± ±
± ± ± ± ± ±
± ± ± ± ± ±
4.121 3.237 3.249 3.249 3.249 3.249
0.099 0.096 0.096 0.096 0.096 0.096
0.259 0.262 0.262 0.262 0.262 0.262 1000 211.089 211.676 211.676 211.676 211.676 211.676
1000 183.646 184.158 184.158 184.158 184.158 184.158
± ± ± ± ± ±
± ± ± ± ± ±
0.1 202.098 201.695 201.695 201.695 201.695 201.695 ± ± ± ± ± ±
10 268.932 265.048 265.102 265.101 265.101 265.101 ± ± ± ± ± ±
0.1 0.069 212.403 ± 0.096 212.339 ± 0.096 212.339 ± 0.096 212.339 ± 0.096 212.339 ± 0.096 212.339 ± iterative output
0.282 0.262 0.262 0.262 0.262 0.262
4.090 3.237 3.249 3.249 3.249 3.249
100 267.506 265.068 265.101 265.101 265.101 265.101
10 212.400 212.339 212.339 212.339 212.339 212.339
± ± ± ± ± ±
± ± ± ± ± ±
3.781 3.241 3.249 3.249 3.249 3.249
0.127 0.124 0.124 0.124 0.124 0.124
100 212.373 212.339 212.339 212.339 212.339 212.339
0.006 0.004 0.004 0.004 0.004 0.004
0.126 0.124 0.124 0.124 0.124 0.124
± ± ± ± ± ±
± ± ± ± ± ±
10 100 0.013 202.078 ± 0.012 201.897 0.004 201.695 ± 0.004 201.695 0.004 201.695 ± 0.004 201.695 0.004 201.695 ± 0.004 201.695 0.004 201.695 ± 0.004 201.695 0.004 201.695 ± 0.004 201.695 iterative output at α = 0.7
iterative output at α = 0.3
0.127 0.124 0.124 0.124 0.124 0.124
± ± ± ± ± ±
± ± ± ± ± ±
1 202.096 201.695 201.695 201.695 201.695 201.695
1 0.127 212.403 0.124 212.339 0.124 212.339 0.124 212.339 0.124 212.339 0.124 212.339 at α = 0.9
0.013 0.004 0.004 0.004 0.004 0.004
Eα(i) represents the ith iterative output (i = 1, ..., 6). The iterative output is the “mean value ± 95% confidence interval”.
Eα(0) Eα(1) Eα(2) Eα(3) Eα(4) Eα(5) Eα(6)
Eα(0) (kJ mol−1) Eα(1) (kJ mol−1) Eα(2) (kJ mol−1) Eα(3) (kJ mol−1) Eα(4) (kJ mol−1) Eα(5) (kJ mol−1) Eα(6) (kJ mol−1) iteration
(kJ mol ) (kJ mol−1) (kJ mol−1) (kJ mol−1) (kJ mol−1) (kJ mol−1) (kJ mol−1) iteration
Eα(0) Eα(1) Eα(2) Eα(3) Eα(4) Eα(5) Eα(6)
iteration
1000 257.452 265.207 265.100 265.101 265.101 265.101
1000 212.109 212.339 212.339 212.339 212.339 212.339
1000 200.229 201.698 201.695 201.695 201.695 201.695
± ± ± ± ± ±
± ± ± ± ± ±
± ± ± ± ± ±
1.417 3.272 3.248 3.249 3.249 3.249
0.115 0.124 0.124 0.124 0.124 0.124
0.050 0.004 0.004 0.004 0.004 0.004
Table 2. Iterative Outputs of the Cai−Chen Isoconversional Analysis of the Pyrolysis of Corn Stover with Significantly Different Values of Eα(0) with Δα = 0.01 at α = 0.1, 0.3, 0.5, 0.7, and 0.9a
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Figure 2. Cai−Chen convergent isoconversional plots at different α (α varies from 0.05 to 0.95 with a step of 0.05) for six heating rates (2.5, 5, 10, 20, 40, and 80 K min−1).
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Figure 3. Effective activation energies of the pyrolysis of eight lignocellulosic biomass samples.
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Table 3. Values of α1, α2, E0, E1, and E3 for the Pyrolysis of Eight Lignocellulosic Biomass Samples
α1 α2 E0 (kJ mol−1) E1 (kJ mol−1) E3 (kJ mol−1)
corn stover
cotton stalk
palm oil husk
pine wood
red oak
sugar cane bagasse
switch grass
wheat straw
0.425 0.725 181.9 211.7 ± 0.480 273.6
0.325 0.700 181.0 208.4 ± 0.644 284.6
0.300 0.675 176.2 203.5 ± 0.818 281.4
0.450 0.825 182.3 216.5 ± 0.683 280.6
0.425 0.800 179.6 211.9 ± 0.474 260.2
0.450 0.825 185.4 215.5 ± 0.563 280.0
0.450 0.725 190.3 217.1 ± 0.455 300.9
0.400 0.675 180.8 208.7 ± 0.454 287.0
results showed that the activation energies obtained from the isoconversional method were independent of the range of heating rates for all lignocellulosic biomass samples, which indicated that the isoconversional kinetic analysis was valid for lignocellulosic biomass pyrolysis. The effective activation energies obtained by the isoconversional method for the pyrolysis of lignocellulosic biomass vary strongly with the extent of conversion. In general, the effective activation energies increase with increasing the extent of conversion, except that, in the medium range of conversion, the effective activation energies exhibit a practically constant value.
energies change strongly with the extent of conversion for all lignocellulosic biomass samples. One can distinguish three ranges of conversion, namely, (1) the range 0.05 ≤ α ≤ α1, where Eα increases from E0 to E1; (2) the range α1 < α ≤ α2, where Eα exhibits a practically constant value of E1; and (3) the range α2 < α ≤ 0.95, where Eα increases from E1 to E3. The meanings of α1, α2, E0, E1, and E3 are shown in Figure 4, and their values for eight biomass samples are listed in Table 3.
■
AUTHOR INFORMATION
Corresponding Author
*Telephone: +86-21-34206624. E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
■
ACKNOWLEDGMENTS Financial support was obtained from the National Natural Science Foundation of China (Grant 50806048), the State Key Laboratory of Heavy Oil Processing, China University of Petroleum (Grant 2012-1-02), and the School of Agriculture and Biology, Shanghai Jiao Tong University (Grant NRC201101). Ronghou Liu was supported by the National Natural Science Foundation of China (Grant 51176121).
■
Figure 4. Variation of effective activation energies with conversion for lignocellulosic biomass pyrolysis.
REFERENCES
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Such a trend of the variation of the effective activation energies with the conversion degree for lignocellulosic biomass pyrolysis may be associated with the overlapped decomposition reactions of hemicellulose, cellulose, and lignin. Further research will be presented in our next work. This similar trend of the variation of the effective activation energies with the conversion degree for lignocellulosic biomass pyrolysis was found in the work by Chen and Cai.37 They applied three isoconversional methods to study the pyrolysis kinetics of sweet sorghum bagasse, and the obtained effective activation energies ranged from 150 to 220 kJ mol−1 in the conversion degree range of 0.15−0.85. Our results are also similar to the results of the pyrolysis of sugar cane bagasse38 and pecan shells.39
5. CONCLUSION The Cai−Chen iterative linear integral isoconversional method was employed for the isoconversional kinetic analysis of eight processes, each of which involved three-parallel DAEM reactions. Those parallel DAEM reactions are linked to the pyrolysis of biomass components, i.e., hemicellulose, cellulose, and lignin. In our previous work,17 those processes reproduced the pyrolysis kinetic behaviors of eight lignocellulosic biomass samples (corn stover, cotton stalk, palm oil husk, pine wood, red oak, sugar cane bagasse, switch grass, and wheat straw). The 3922
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dx.doi.org/10.1021/ef5005896 | Energy Fuels 2014, 28, 3916−3923
