Artificial neural network model for the prediction of kinetic parameters of biomass pyrolysis from its constituents

Fuel 193 (2017) 142–158

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Full Length Article

Artificial neural network model for the prediction of kinetic parameters of biomass pyrolysis from its constituents Sasithorn Sunphorka a,b, Benjapon Chalermsinsuwan a,c, Pornpote Piumsomboon a,c,⇑ a

Fuels Research Center, Department of Chemical Technology, Faculty of Science, Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok 10330, Thailand Faculty of Engineering and Architecture, Rajamangala University of Technology Tawan-ok, Uthenthawai Campus, 225 Phayathai Road, Bangkok 10330, Thailand c Center of Excellence on Petrochemical and Material Technology, Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok 10330, Thailand b

h i g h l i g h t s

g r a p h i c a l a b s t r a c t


Artificial neural network models were

developed to predict biomass pyrolysis.
A total of 150 data from different sources were considered.
The relative importance of each biomass constituent was determined.
Contour plots were generated to predict the kinetic values from biomass composition.

a r t i c l e

i n f o

Article history: Received 2 September 2016 Received in revised form 14 December 2016 Accepted 17 December 2016

Keywords: Artificial neural network Biomass Kinetics Modeling Pyrolysis

a b s t r a c t This study applied artificial neural networks (ANN) for constructing the correlation between biomass constituents and the kinetic parameters (activation energy (Ea), pre-exponential factor (k0) and reaction order (n)) of biomass pyrolysis. Three ANN models were developed, one for each of the three kinetic parameters. A total of 150 experimental thermogravimetric analyses from a diverse range of biomass compositions were used to develop and test the networks. The relationships between the main biomass components and the output parameters were non-linear and could potentially be predicted by the selected ANN models (R2 > 0.9). Using a mean standard error limit of 0.001, the number of neurons in the hidden and the output layer and the model parameter weights and biases were optimized, with 20, 17 and 30 neurons, for log k0, log Ea and log n, respectively. The generated contour plots revealed that cellulose required the highest k0, Ea and n values, as well as the non-linearity and complexity of the system. Ó 2016 Elsevier Ltd. All rights reserved.

1. Introduction Lignocellulosic materials, and especially agricultural waste materials, are widely considered as future resources of sustainably renewable and clean energy, biomaterials, and also feedstocks. Pyrolysis is one suitable technique for the conversion of biomass

⇑ Corresponding author at: Fuels Research Center, Department of Chemical Technology, Faculty of Science, Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok 10330, Thailand. E-mail address: [email protected] (P. Piumsomboon). http://dx.doi.org/10.1016/j.fuel.2016.12.046 0016-2361/Ó 2016 Elsevier Ltd. All rights reserved.

for (mostly) energy production. Furthermore, pyrolysis is also involved in other thermal conversion processes of biomass, such as oxidation and gasification, as the main reaction of thermal decomposition. However, the variation in the main components of each different biomass source plays an important role on the biomass properties including its pyrolysis. During the reaction, each component decomposes at a different rate and temperature range due to its structure and composition [1]. The pyrolysis of biomass is very complex process that involves several different kinds of component, including cellulose, hemicelluloses, lignin and other extractive and combined materials, and also involves several different reaction pathways. The diversity,

S. Sunphorka et al. / Fuel 193 (2017) 142–158

heterogeneity and limited thermal stability of each component influence the overall pyrolysis behavior of each biomass with different compositions [2]. Cellulose, hemicellulose and lignin are the main components (by mass) of lignocellulosic biomasses, therefore, they each present a different thermal behavior. Cellulose consists of several forms of glucose whilst hemicellulose consists of various saccharides [3,4]. Both of them are pyrolyzed in the temperature ranging of 180–370 °C [5]. Hemicellulose is chemically most active and starts to degrade at lower temperature with a major degradation at above 200 °C. Cellulose degrades at higher temperature (>275 °C) and has a major degradation at narrow temperature band of 270–350 °C [6]. Unlike cellulose and hemicellulose, the structure of lignin is more complex being a highly cross-linked polyphenolic aromatic polymer with no ordered repeating units. It is accordingly decomposed over a wider temperature range (175–890 °C) [3,5]. Many literature studies have reported the kinetic constants for the pyrolysis of individual biomass components [7–9]. The thermogravimetric analysis was used to examine the thermal behavior of biomass and kinetic values were calculated by n-order method [7,10] or other equations. For example, in the pyrolysis of soft and hard woods it was reported that, of the different constituents, cellulose had the highest preexponential factor and activation energy while lignin had the lowest [11]. Even though the pyrolysis of individual components has been explored, the predicted thermal conversion profile and product distribution of biomass pyrolysis generally does not correspond to the actual one [12,13]. The main explanation for this discrepancy is due to the interaction between the main components themselves and the non-linear correlation between the biomass composition and kinetic parameters. From a logistic and economic point of view, it is necessary for the process design and scale-up to first theoretically simulate the thermal decomposition behavior of biomass by mathematical models prior to iterative testing with actual scale experimental systems. The parametric estimation of the pyrolysis behavior of biomass from its composition has been addressed [8,9,12,14], but the predicted values were typically found to only poorly correspond with the experimental values due to several factors. For example, a one-step multi-component pyrolysis model for the independent degradation reactions of hemicellulose, cellulose and lignin revealed a different calculated biomass composition from the estimated values, especially in the case of lignin [9]. This was because the extractives that have comparable decomposition characteristics to lignin were included in the calculated amount of lignin, resulting in an overestimation. Indeed, it is difficult to determine the product yields of biomass pyrolysis from individual datasets due to the interactions among compounds [15]. Interaction between cellulose and lignin during slow and fast pyrolysis was examined by Hilbers et al. [16]. Adding lignin to cellulose influenced the production of many methoxylated monophenols, levoglucosan and lignin dehydration products such as 5hydrosymethylfurfural and furfural. It was probably because of the enhance micro-explosions and decrease in the residence time of cellulose derived products in the liquid intermediates. The prediction of the characteristics and models for biomass pyrolysis from these compositions is, however, still an attractive option. Besides the interactions among the different components in any given biomass, some of the major problems for model development include (i) the complex and non-linear relations between the main components and the kinetic parameters (ii) the lack of coverage in the available data across the range of biomass diversity [9,12]. Currently, artificial neural networks (ANNs) are widely used in various applications as a tool for estimating the non-linear relationship between the input and output data because they can approximate arbitrary nonlinear functions. Other advantages of using ANNs are that they do not require a mathematical descrip-

143

tion of the phenomena involved and their learning and adaptability ability allow the systems to update themselves [17–19]. However, from the best knowledge of the authors, there is not any study that provided information on the use of or suitable ANN models for the prediction of the thermal behavior of biomass from its principal constituents [20–22]. Sun et al. [20] studied the pyrolysis of pine sawdust. The input parameters included space velocity, reaction temperature, and particle size while the output parameters were four gas products (H2, CO, CH4, CO2). Çepeliog ullar et al. [21] investigated the thermal behaviors of a highly˘ heterogeneous fuel or refuse-derived fuel (RDF). The input data was temperature and heating rate while the output was temperature dependent weight loss. The results reported in these works demonstrated the high efficiency of ANN to predict the output parameters with very high regression value (R2). However, the studies had limitation about biomass type and the description of biomass pyrolysis behavior or their kinetic values which is the important information for the process control. As described above, the application of non-linear modeling would be interesting to predict the behavior of the complicated model. Therefore, this work aimed to construct ANN models for estimating the kinetic parameters, which referred to the devolatilization kinetics, of biomass pyrolysis based upon their main components of cellulose, hemicellulose and lignin as inputs. To develop a likely more suitable model, the employed data would have to be carefully selected so as to represent the wide range of different types and compositions of biomass. The input data, kinetic values, were obtained by calculations from the published experimental thermogravimetric analyses (TGA) as shown in Appendix. The TGA data of more than 60 samples with different TGA conditions (totally 150 data) was selected. Therefore, the proposed models were derived based on each component ratios of 0– 100 wt.%, since the pure components and their mixtures were also tested. The ANN models were investigated in detail and the important parameters were determined. In addition, the predictive ability of the models was tested with another set of experimentallyderived TGA biomass data and contour plots for the predicted kinetic parameters were proposed. The proposed ANN models can give kinetic information in a very faster way compared to other methods such as calculated from TGA data as only weight percentage of biomass components was required in this case.

2. Methods 2.1. Experimental data selection In this study, the set of experimental data that were used to develop and test the ANNs (Tables A1–A3, Appendix) were selected from a diverse range of different biomass types. The input data was the mass percentage composition of cellulose, hemicellulose and lignin in the biomass, whilst the output data were the kinetic parameters of the pre-exponential factor (k0), activation energy (Ea) and reaction order (n), as summarized in Tables A1–A3, respectively. Since the data required for ANNs must be adequate and accurate, a large number of experimental data sets were selected and all the kinetic parameters were recalculated as appropriate by the same analytical method (Eq. (1), see below) to standardize the method across the different biomasses. The different inputs were selected for each of the three single outputs to obtain the best fit model for each output and so reduce the high complexity of the model. In total 150 experimental datasets were chosen. The kinetic parameters of the pure components, mixed pure components and real biomass were evaluated. Some of the kinetic parameters presented in Tables A1–A3 (Appendix) are not the same as those originally reported in the references because they

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were recalculated using the standardized analytical model., the TGA data obtained from all references was extracted and fitted by the standardized analytical model developed from an orderbased model [23,24], as shown in Eq. (1), to estimate all new kinetic parameters. This analytical model is respected to the general expression of decomposition rate of solid materials which were also chosen by several literatures [7,25–28].

(

a¼1

k0 RT 2 1  ðn  1Þ  bEa

!

1   )1n 2RT Ea 1 exp  ; Ea RT

ð1Þ

where Ea represents activation energy, k0 represents preexponential factor, n represents reaction order, T represents temperature (K), R represents gas constant =0.008314 kJ/K mol and b represents heating rate. The a is the mass fraction of decomposed biomass at any time =(w0  w)/(w0  wf) whilst w0 represents the initial substrate mass, w represents the mass of substrate present at any time t and wf represents the final mass of solids. Hereafter, Eq. (1) is referred to as the analytical model. From TGA data, the sample weight at any time was converted to a. The desired values of heating rate (b), R and T were substituted to Eq. (1) and then fitted the modified TGA data with the model by using Curve Fitting ToolboxTM in the MATLAB software to obtain kinetic values as output data. After calculation, the output data (k0, Ea and n) was scaled within the proper range by taking the logarithm (log k0, log Ea and log n). The input-output data examination was performed by ANNs as described in Section 2.2. 2.2. Methodology of ANN development To predict the values of log k0, log Ea and log n, ANNs were considered since these have been reported to be an effective tool for predicting complex and non-linear input-output correlations. An ANN is a processing tool based on the mammalian neural networks, which has ability to learn from sensory feedback and develop itself, but at much smaller scale. The model was developed using the Neural Network Toolbox in Matlab, and an example of the architecture of the model for each kinetic parameter is shown schematically in Fig. 1 for a fully connected system with only one hidden layer and one output layer. The data always flow from the input layer through the output layer. Therefore, three interconnecting sets of ANN were set up using the same input sets of data, but mapping to the different output data (the kinetic parameters k0, Ea and n). As mentioned in Section 2.1, the input data in the current work were the mass percentage composition of cellulose, hemicellulose

and lignin in the biomass. The input layer was scaled and introduced into the hidden layer via weight, obtained by Neural Network Toolbox and shown in Table 1. Then, the neural in the hidden layer sum up the weighted inputs. The bias is also connected into all neurons in the subsequent layer and provides additional adjustable parameters. After that, the weighted output, including the bias from the hidden neurons, was passed through the neuron in the output layer. The output values were produced from the output neuron layer via the same procedure as that in the hidden layer. In all models, the transfer functions of the hidden layer and output layer were chosen. A hyperbolic tangent sigmoid function (tansig) was used in the hidden layer whilst a linear transfer function (purelin) was used in the output layer since the combination of tansig/purelin transfer function provided the lowest mean square error (MSE) in many cases [29,30]. The equations of tansig and purelin are shown in Eqs. (2) and (3), respectively [30]:

tansigðnÞ ¼

2 ; 1 þ expð2nÞ  1

ð2Þ

purelinðnÞ ¼ n:

ð3Þ

The number of neurons in the hidden layer was calculated by trial and error and minimizing MSE of models to less than 0.001. The weights and bias of inputs and each neuron were also adjusted in order to minimize the error. These parameters were used to simulate the output values by the proposed model as shown in Eq. (4);

" ! !# ! j¼n i¼3 X X output ¼ purelin LW 1;j : tansig IW j;i :pi þ B1;j þ B2 ; j¼1

i¼1

ð4Þ where pi is the input variable, B1,j represents bias of inputs (1) to neurons (j) in hidden layers, B2 represents bias of output layer, IWj,I represents weight of inputs (i) to hidden layer (j) and LW1,j represents weight of neurons (j) in hidden layer to output layer (k = 1). However, a single output layer was fixed, being the selected output (log k0, log Ea and log n), with one neuron in the output layer. To improve the prediction ability of the models, the data was randomly separated into training (70%), validation (15%) and test (15%) subsets. The network was trained using the LevenbergMarquardt backpropagation algorithm, which is an intermediate optimization algorithm between the Gauss–Newton method and gradient descent algorithm. The performance of the models in all data sets was statistical measured by the linear regression coefficient (R2), following comparison between the experimental and predicted values.

Fig. 1. Schematic representation of an example ANN model structure to predict the kinetic parameters of k0, Ea and n for the pyrolysis of biomass. Shown are the three inputs with their neurons (i), interlinked connections from each input to all hidden layer neurons (j) along with the selected weightings. The weighted outputs were then merged and feed into the output neuron (k) to form the output values.

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S. Sunphorka et al. / Fuel 193 (2017) 142–158 Table 1 Weights and biases used in the ANN models for the determination of: (A) Log k0, (B) Log Ea and (C) Log n.

a

j

IWj,1

IWj,2

IWj,3

B1,j

B2

LW1,ja

(A) Log k0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

4.2126 3.7043 3.3817 2.3525 1.1478 1.4574 3.3231 4.0986 2.6292 2.8104 1.8715 0.7568 0.9918 0.9647 2.444 4.8283 0.8082 3.0477 4.3206 2.3687

1.0147 0.6537 2.4259 2.4697 2.9315 2.7174 2.4383 1.4783 0.8045 1.4904 0.5949 4.8614 1.8328 2.91 3.4043 2.1448 1.2123 1.2685 3.4733 2.0604

2.2202 2.6646 1.0992 1.6757 1.6566 2.2708 0.237 1.0075 4.4773 0.4469 2.1849 3.8105 6.0214 4.4118 3.211 1.4273 4.3938 2.4707 1.7955 1.9664

3.8885 4.3115 1.8284 2.6014 2.4456 2.0484 1.6042 1.0445 2.9915 2.8134 1.0786 0.0969 0.9885 2.074 2.7175 0.2488 1.5859 1.2865 1.757 3.9623

0.5725

1.4097 3.1156 2.3492 0.4738 1.2731 0.8307 0.5132 0.6972 1.4070 0.8084 2.2285 0.2964 0.8792 1.0764 2.6408 1.5892 1.5823 2.0537 0.6970 2.0571

(B) Log Ea 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1.1014 2.3545 1.1258 0.7845 0.3993 0.283 2.0566 1.0626 0.1367 0.7155 2.979 0.5398 0.8238 3.1555 3.5479 3.7851 2.8008

2.1138 0.7693 1.4171 3.7105 0.7042 3.914 3.4762 0.3662 2.5801 2.6237 0.0832 2.8209 0.3235 0.0844 1.624 0.5897 0.1329

2.6975 1.7621 3.1113 0.1605 3.5076 1.3428 1.3239 2.9169 2.5867 2.5376 1.9275 1.6906 3.5313 1.93 0.359 1.2893 2.2184

3.5997 3.1386 2.7002 1.8243 1.9267 1.6754 0.8252 1.0865 0.0005 0.6126 1.1555 2.1984 1.7843 2.1976 2.44 2.8128 3.5974

0.1567

0.5005 1.0932 0.3400 1.5099 0.5896 0.5881 0.4769 0.6728 0.9407 1.0296 0.0789 0.1504 0.3335 0.9755 0.8983 0.9581 0.0100

(C) Log n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

3.79 1.7021 0.587 2.737 4.1345 0.8885 3.1522 2.4489 2.1176 2.0592 4.1381 1.719 1.4988 5.4787 1.1189 1.4304 2.5622 1.1099 3.2207 1.7145 2.8516 1.4107 1.7501 1.0969 1.4465 2.8126 0.54 1.7384 1.8076 0.6574

0.6602 0.7493 0.5952 3.6141 0.7526 0.4312 2.1001 3.1108 1.8311 3.7795 1.5669 3.9863 4.1296 0.0632 2.7342 4.1355 0.8144 2.0584 2.3855 2.7555 1.7197 0.4227 2.8602 3.8181 4.0438 2.7268 3.2669 1.8499 2.9456 0.3751

1.9484 2.7939 4.2383 0.0715 2.3547 3.8559 2.306 1.5667 3.3273 0.2414 1.8028 0.2799 0.4181 1.8056 3.3203 3.7711 3.6228 3.7819 1.4203 2.4361 2.98 4.0851 2.3663 0.8539 0.1431 1.9827 2.6586 3.3482 2.9983 4.1705

4.4102 5.1864 3.7942 4.0049 3.1692 3.2519 2.8124 2.3676 1.9439 1.8794 1.3276 0.9845 0.6144 2.3032 0.3567 0.8518 0.0069 0.7301 1.1125 1.6867 1.8922 1.9103 2.6439 2.4272 2.3419 3.0968 3.6922 3.8888 3.8115 4.4565

0.3726

0.3593 0.4609 0.3057 1.8034 1.4334 0.4979 1.5141 0.0846 0.4889 0.5113 0.3807 0.2550 0.8263 1.2676 0.8537 0.5288 0.5252 0.6110 0.0719 0.6042 0.4345 0.1627 0.1843 1.5891 0.9944 0.5300 0.6580 0.3153 0.5004 0.5803

LW1,j is the row vector.

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2.3. The ANN accuracy test The accuracy of proposed ANN model was tested by comparison with the TGA profile of another biomass. The pyrolysis of rice straw (51.2 wt.% cellulose, 23.29 wt.% hemicellulose and 23.58 wt.% lignin) was analyzed by thermogravimetric/differential thermal analyzer (Mettler Toledo TG Analyzer 851e model) under N2 atmosphere. The flow rate of N2 gas was 50 mL/min. The sample was heated from 30 to 800 °C at linear heating rates of 10 K/min. 3. Results and discussion 3.1. The selected ANN models for each kinetic parameter The effect of the number of neurons in the hidden layer on the MSE for each output are shown in Fig. 2, from which the number of neurons were selected as 20, 17 and 30 for log k0, log Ea and log n, respectively, based upon obtaining a MSE of less than 0.001. Table 1 shows the obtained parameters of the weight of inputs to hidden layers (IWj,i), weight of neurons in the hidden layer to output layer (LW1,j), bias of inputs (B1,j) and bias of output layer (B2). These parameters were used to calculate the output values that had the best fit to the experimental values for log k0, log Ea and log n (Figs. 3–5, respectively). The summation of regression coefficients (R2) for each output showed a good agreement between the experimental values and predicted values (R2 P 0.90). However, note that the variation of k0 with biomass composition was much higher than that of Ea and n (Tables A2 and A3, respectively). Therefore, even though the R2 for the prediction of k0 was higher than 0.9, it tended to fit less well compared to that for Ea and n. All connection weights were used to determine the relative importance of the input parameters to the desired output using the Garson equation [31], as shown in Eq. (4);

   jIW j;i j :jLW P j 1;j i¼3 j¼1 jIW i;j j   i¼1  ; Ii ¼ Pi¼3 Pj¼n jIW j :jLW 1;j j Pi¼3 j;i i¼1 j¼1 Pj¼n

i¼1

ð4Þ

jIW j;i j

where Ii is the relative influence of each input (cellulose, hemicellulose or lignin). The weights were taken to absolute values. The numerator represents the sum of the absolute weights of each input whilst the denominator represents all the absolute weights feeding into hidden layers. The calculated relative importance of all the

inputs to each output revealed that all input variables had relatively the same order of influence on the values of log Ea (Fig. 6). However, for log k0, cellulose had the strongest effect (40%). It was because cellulose composes of one simple repeating unit, cellubiose, resulting in the rapid decomposition within a short temperature range and gave high k0 value. For log n, hemicellulose had the most influence (37%) followed by cellulose (34%). Even though the effect of these three main factors could be determined, the interaction between them could not be reported by this correlation. Therefore, contour plots were generated in order to observe any relationship between the input parameters and each response (Section 3.2).

3.2. Application of the ANN models to other biomass materials and contour plots of the estimated kinetic parameters Besides the validation and test data set, a new set of biomass data was chosen to check the accuracy of the model. The conversion profile derived from the available TGA data of rice straw is shown in Fig. 7. The experimental data was fitted by the analytical model (Eq. (2)) to obtain the kinetic parameters, whilst the predicted values were generated by substitution of the kinetic parameters calculated from the ANN models into the analytical model. The calculated k0 (6.80  106 s1), Ea (93.52 kJ/mol) and n (1.50) values from the ANN models gave a curve with a comparable trend to the curve fitted by the analytical model, and both were in good agreement to the experimental data with high R2 values. The mean percentage errors (MPEs) of the models obtained from the fitting by the analytical model and calculating by ANN were approximately 11% and 14%, respectively. This indicated that the predicted values were acceptable. However, Fig. 7 illustrated the error of predicted data at very low and high temperature. It was due to the assumption of the analytical model which was developed based on single-step decomposition. Generally, the biomass pyrolysis involves several degradation steps. The moisture evaporation prior occurred at very low temperature whilst degradation of main component occurred at individual temperature range, as mentioned in Introduction. Nevertheless, the predicted data was highly accurate at the session that major degradation occurred. The derived contour plots for the relationship between the inputs and each output (log k0, log Ea and log n) are shown in Fig. 8. The log Ea values for the pyrolysis of pure cellulose, hemicelluloses and lignin were approximately 2.26, 1.99 and 2.13, respectively, whilst they were 15.10, 8.90 and 7.85, respectively, for log k0

Fig. 2. Selection of the number of neurons in the hidden layer for the ANN determination of the log k0, log Ea and log n.

S. Sunphorka et al. / Fuel 193 (2017) 142–158

147

Fig. 3. Comparison of the experimental results and predicted results derived from the ANN model for the log k0 in the (a) training, (b) validation and (c) test periods, as well as in (d) all three combined.

Fig. 4. Comparison of the experimental results and predicted results derived from the ANN model for the log Ea in the (a) training, (b) validation and (c) test periods, as well as in (d) all three combined.

and 0.38, 0.04 and 0.26, respectively, for log n. That the highest k0, Ea and n were required for the pyrolysis of pure cellulose is due to its crystalline structure of cellulose [32]. The crystalline micro-

fibril arrangement makes cellulose more resistant to thermal decomposition than hemicellulose or lignin. Although cellulose starts to devolatilize at a higher temperature than hemicellulose

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S. Sunphorka et al. / Fuel 193 (2017) 142–158

Fig. 5. Comparison of the experimental results and predicted results derived from the ANN model for the log n in the (a) training, (b) validation and (c) test periods, as well as in (d) all three combined.

Fig. 6. Relative impact (%) of the different input variables on the outputs of Log Ea, Log k0 and Log n.

and lignin, it then dramatically decomposes within a narrow temperature range because it has a simple repeating unit (cellubiose) and so yields high Ea and k0 values. The high complex and amorphous structure of lignin leads to the thermal decomposition of itself starting at a low temperature but slowly decomposing over a wide temperature range [3]. Therefore, its decomposition rate was lower than that of cellulose and hemicelluloses. Moreover, the phenylpropane structure in lignin decomposes at a high temperature. Therefore, the average Ea of lignin pyrolysis was higher than that of hemicelluloses. In addition, the plot shows the non-linearity and complexity of the inputoutput system. The other points in the ternary plot present the prediction of each kinetic parameter at a different biomass composition with expected high agreement to the actual values.

Fig. 7. Comparisons between the experimental TGA results, the fitted curve obtained by the analytical method (Eq. (2)) and the predicted curve obtained by the ANN model, for the pyrolysis of rice straw.

4. Conclusions Models based upon ANNs are a very useful method for predicting the kinetic parameters of biomass pyrolysis from the biomass composition. Three ANN models were developed for the evaluation of the kinetic parameters (activation energy, pre-exponential factor and reaction order) of biomass pyrolysis. The results obtained from these ANN models showed a high correlation with the published experimental TGA values. The network also presented a comparable trend in the thermal decomposition behavior of selected biomass. All components had an effect on the activation energy. Cellulose had a strong effect on the pre-exponential factor, whilst hemicellulose had a strong effect on the reaction order. The

S. Sunphorka et al. / Fuel 193 (2017) 142–158

Fig. 8. Contour plot for (a) log k0, (b) log Ea and (c) log n.

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S. Sunphorka et al. / Fuel 193 (2017) 142–158

contour plots indicated that the highest activation energy, preexponential factor and reaction order values were required for pure cellulose.

Council of Thailand for financial support. The authors also thank to Dr. Robert Douglas John Butcher (Publication Counseling Unit, Chulalongkorn University) for English language editing.

Acknowledgement Appendix A The authors would like to express their thanks to the Graduate School, Chulalongkorn University and the National Research

See Tables A1–A3.

Table A1 The input data for calculating pre-exponential factor (k0). No.

Biomass

Cellulose (wt.%)

Hemicellulose (wt.%)

Lignin (wt.%)

k0 (s1)

Ref.

1 2

Hornbeam b = 2 K/min Lignin from barley b = 2 K/min Lignin from walnut b = 2 K/min Cellulose from hornbeam b = 2 K/min Switchgrass b = 10 K/min Wheat straw b = 10 K/min Hemicellulose from wheat straw b = 10 K/min Date seed b = 10 K/min Pine (softwood) b = 10 K/min Maple (hardwood) b = 10 K/min Bamboo b = 10 K/min Jute b = 10 K/min Hemp b = 10 K/min Physic nut waste b = 5 K/min Almond shells b = 10 K/min Sugar beet b = 10 K/min Bagasse b = 10 K/min Wheat straw b = 10 K/min Olive stones b = 10 K/min Willow b = 20 K/min Corn stalk b = 20 K/min Wheat straw b = 10 K/min Pinewood b = 10 K/min Pine cone b = 10 K/min Cellulose b = 20 K/min Lignin b = 20 K/min Rice straw b = 10 K/min Dairy manure b = 10 K/min Rice bran b = 10 K/min Chicken manure b = 10 K/min Bamboo b = 20 K/min Rice husk b = 10 K/min Sisal b = 10 K/min Cellulose b = 5 K/min Hemicellulose b = 5 K/min Fruit shell b = 5 K/min C:H = 0.67:0.33 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C:H:L = 0.67:0.17:0.17 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) C:H:L = 0.17:0.17:0.67; b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min

48.90 0.00

23.30 0.00

20.10 100.00

2.24E+08 6.69E+08

[33] [33]

0.00

0.00

100.00

3.83E+08

[33]

100.00

0.00

0.00

1.00E+19

[33]

38.46 39.18 0.00 20.00 43.00 48.00 45.70 66.25 72.30 56.31 57.00 42.00 74.00 66.00 57.00 40.90 42.40 34.60 39.00 32.70 100.00 0.00 21.19 9.96 16.19 7.75 43.30 28.60 73.80 100.00 0.00 45.40 67.00

31.04 26.30 100.00 55.00 28.00 29.00 25.90 16.20 20.15 17.47 10.00 17.00 6.00 10.00 18.00 19.40 29.60 29.30 34.00 37.60 0.00 0.00 29.55 23.48 17.08 19.23 24.60 28.60 11.00 0.00 100.00 6.40 33.00

21.40 17.17 0.00 23.00 30.50 26.50 24.95 12.40 4.70 23.91 33.00 21.00 20.00 24.00 25.00 32.70 21.70 21.30 12.00 24.90 0.00 100.00 4.88 10.18 7.85 5.10 26.20 24.40 9.70 0.00 0.00 30.10 0.00

5.78E+06 5.01E+06 1.00E+11 7.71E+07 1.76E+07 5.99E+06 4.04E+07 9.92E+09 9.92E+13 1.86E+07 1.18E+07 9.04E+05 1.99E+06 6.53E+06 1.36E+07 1.52E+07 1.17E+09 9.05E+08 4.73E+06 1.18E+07 1.00E+20 2.13E+07 7.29E+07 2.03E+09 4.54E+10 1.00E+11 5.83E+07 1.01E+08 1.76E+09 1.00E+20 1.16E+11 5.95E+07

[13]

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

38

39

2.63E+07 1.34E+08 3.47E+07 2.80E+07 2.51E+07 4.15E+06 3.85E+07 4.26E+07 67.00

17.00

17.00

[34] [25] [35] [35] [35] [35] [35] [26] [36] [36] [36] [36] [36] [37] [37] [38] [38] [39] [40] [40] [41] [41] [41] [41] [42] [43] [44] [45] [45] [46] [46]

[47]

[47] 7.69E+06 3.99E+07 4.29E+07 4.05E+07 4.65E+07 6.90E+07 5.09E+07

17.00

17.00

67.00

[47] 1.38E+07 4.29E+07 6.33E+08 9.58E+08

151

S. Sunphorka et al. / Fuel 193 (2017) 142–158 Table A1 (continued) No.

40

41

43

44

45

46

47

48

49

Biomass b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) H:L = 0.33:0.67 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C:L = 0.67:0.33 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C:L = 0.33:0.67 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C = H = L = 0.33 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C:H = 0.33:0.67 b = 5 K/min b = 10 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) H:L = 0.67:0.33 b = 5 K/min b = 10 K/min b = 20 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C:H:L = 0.17:0.67:0.17 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) Giant Leuciana b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) Hornbeam wood

Cellulose (wt.%)

Hemicellulose (wt.%)

Lignin (wt.%)

k0 (s1)

Ref.

2.61E+07 8.94E+07 2.96E+07 7.88E+08 0.00

33.00

67.00

[47] 2.18E+07 2.16E+07 4.13E+07 3.63E+07 1.95E+07 2.16E+07 4.13E+07 3.63E+07

67.00

0.00

33.00

[47] 3.24E+07 4.28E+07 5.44E+07 4.81E+07 3.86E+07 5.10E+07 5.33E+07 3.71E+07

33.00

0.00

67.00

[47] 2.86E+07 3.85E+07 4.01E+07 4.00E+07 3.58E+07 4.03E+07 3.97E+07 3.73E+07

33.00

33.00

33.00

[46] 5.04E+07 7.50E+07 2.53E+06 1.92E+06 8.48E+07 5.52E+07 3.42E+07 3.31E+07

33.00

67.00

0.00

[46] 5.11E+06 3.32E+07 4.05E+07 6.10E+06 2.84E+07 3.27E+07 1.33E+06

0.00

67.00

33.00

[46] 5.86E+06 2.53E+07 1.87E+07 6.12E+06 2.78E+07 2.82E+07 6.45E+06

17.00

67.00

17.00

[46] 5.94E+07 3.14E+07 1.87E+07 3.54E+07 1.89E+07 2.45E+07 2.82E+07 6.45E+06

41.88

29.93

25.46

[46] 3.23E+07 4.65E+07 3.26E+07 5.54E+07 2.82E+07 3.32E+07 3.41E+07 3.78E+07

48.90

23.30

20.10

[33] (continued on next page)

152

S. Sunphorka et al. / Fuel 193 (2017) 142–158

Table A1 (continued) No.

50

51

52

53 54 55 56 57 58 59

60

61 62 63 64 65 66 67

Biomass b = 2 K/min b = 2 K/min b = 5 K/min Empty fruit Bunches b = 10 K/min b = 50 K/min Palm shell b = 10 K/min b = 50 K/min Oriental white oak b = 15 K/min b = 20 K/min Willow b = 20 K/min Coconut shell b = 20 K/min Coffee residue b = 20 K/min Sawdust b = 20 K/min Rice husk b = 20 K/min Wood b = 10 K/min Palm fiber b = 5 K/min b = 10 K/min b = 15 K/min b = 20 K/min Poplar wood sawdust b = 20 K/min b = 40 K/min b = 60 K/min Bamboo b = 10 K/min P. chrysosporium b = 10 K/min Raw wood b = 30 K/min Corn cob b = 25 K/min African rice husk b = 25 K/min African Jatropha b = 25 K/min Rice straw b = 25 K/min

Cellulose (wt.%)

Hemicellulose (wt.%)

Lignin (wt.%)

k0 (s1)

Ref.

6.78E+06 3.89E+07 1.31E+07 38.30

35.30

22.10

[13] 9.89E+06 9.93E+07

20.80

22.70

50.70

[13] 9.93E+07 9.80E+07

50.40

59.72 41.84 27.38 36.08 36.06 39.10 36.69

14.30

20.06 21.03 40.10 11.18 21.34 28.80 30.51

22.80

20.22 39.17 6.00 28.66 21.16 12.10 32.80

[48] 9.68E+07 1.82E+07 4.12E+06 2.25E+05 2.96E+07 2.01E+06 7.62E+07 2.70E+06

[49] [49] [50] [50] [50] [51] [5]

1.63E+05 1.34E+05 3.11E+06 1.30E+06 44.74

16.73

30.72

[52]

48.00 41.00

23.60 35.00

20.60 24.00

4.77E+07 9.54E+07 9.96E+07 1.29E+06 9.68E+07

40.49 38.26 37.34 10.08 31.84

15.74 46.59 10.07 48.83 7.29

43.77 7.16 41.08 13.96 28.39

1.01E+08 2.39E+05 4.59E+07 2.02E+06 9.39E+07

[53] [54] [55] [56] [56] [56] [56]

Note: (i) C = cellulose, H = hemicelluloses, L = lignin. (ii) The difference in kinetic values obtained from the same biomass and pyrolysis condition was due to the experimental error during TGA test and TGA data fitting with an analytical model. The (2) after b represents the number of replication.

Table A2 The input data for calculating activation energy (Ea). No.

Biomass

Cellulose (wt.%)

Hemicelluloses (wt.%)

Lignin (wt.%)

Ea (kJ/mol)

Ref.

1

Lignin from barley b = 5 K/min Lignin from walnut b = 5 K/min Cellulose from barley b = 5 K/min Cellulose from hornbeam b = 5 K/min Switchgrass Wheat straw Empty fruit bunches b = 10 K/min Fiber b = 10 K/min Hemicellulose from wheat straw b = 10 K/min Date seed b = 10 K/min Oriental white oak Pine (softwood) b = 10 K/min Maple (hardwood) b = 10 K/min Bamboo b = 10 K/min Physic nut waste Almond shells b = 10 K/min Wheat straw b = 10 K/min Olive stones b = 10 K/min Soya bean hulls b = 10 K/min Rape extracted meal b = 10 K/min Willow b = 20 K/min Wheat straw b = 10 K/min Pinewood b = 10 K/min Pine cone b = 10 K/min

0.00

0.00

100.00

127.40

[33]

0.00

0.00

100.00

126.30

[33]

100.00

0.00

0.00

208.40

[33]

100.00

0.00

0.00

250.10

[33]

38.46 39.18 44.97 33.21 0.00 20.00 50.40 43.00 48.00

31.04 26.30 19.92 16.58 100.00 55.00 14.30 28.00 29.00

21.40 17.17 10.23 21.79 0.00 23.00 22.80 30.50 26.50

99.08 98.61 100.20 114.50 123.70 90.85 92.42 106.20 102.70

[13] [13] [57] [57] [34] [25] [48] [35] [35]

45.70 56.31 57.00 66.00 57.00 55.00 47.00

25.90 17.47 10.00 10.00 18.00 16.00 33.00

24.95 23.91 33.00 24.00 25.00 29.00 20.00

103.70 87.94 98.70 102.60 99.02 90.00 84.02

[35] [26] [36] [36] [36] [36] [36]

40.90 34.60 39.00 32.70

19.40 29.30 34.00 37.60

32.70 21.30 12.00 24.90

107.20 103.90 91.71 107.30

[37] [38] [38] [39]

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

153

S. Sunphorka et al. / Fuel 193 (2017) 142–158 Table A2 (continued) No.

Biomass

Cellulose (wt.%)

Hemicelluloses (wt.%)

Lignin (wt.%)

Ea (kJ/mol)

Ref.

25 26 27 28 29

Cellulose b = 20 K/min Rice straw b = 10 K/min Dairy manure b = 10 K/min Rice bran b = 10 K/min Chicken manure b = 10 K/min Bamboo b = 20 K/min Rice straw b = 10 K/min Rice husk b = 10 K/min Corncob b = 10 K/min Sisal b = 10 K/min Cellulose b = 5 K/min Hemicellulose b = 5 K/min Poplar wood b = 10 K/min C: H = 0.67:0.33 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C:H:L = 0.67:0.17:0.17 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C:H:L = 0.17:0.17:0.67 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) C:H:L=0.17:0.17:0.67 b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) H:L = 0.33:0.67 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C:L = 0.67:0.33 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C:L = 0.33:0.67 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C:H:L: 0.33:0.33:0.33 b = 5 K/min b = 10 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2)

100.00 21.19 9.96 16.19 7.75

0.00 29.55 23.48 17.08 19.23

0.00 4.88 10.18 7.85 5.10

262.10 113.70 121.80 139.80 130.30

[40] [41] [41] [41] [41]

43.30 32.00 28.60 50.50 73.80 100.00 0.00 43.00 67.00

24.60 35.70 28.60 31.00 11.00 0.00 100.00 28.00 33.00

26.20 22.30 24.40 15.00 9.70 0.00 0.00 25.00 0.00

104.00 86.64 112.20 105.00 140.20 266.90 117.30 82.34

[42] [43] [43] [43] [44] [45] [45] [58] [47]

30 31 32 33 34 35 36 37 38

39

40

41

42

43

44

45

91.52 94.87 92.98 97.66 89.83 81.25 94.58 97.92 67.00

17.00

17.00

[47] 92.01 87.41 100.90 98.30 98.17 95.97 106.00 101.70

17.00

17.00

67.00

[47] 89.42 90.75 100.30 101.90 94.84

17.00

17.00

67.00

[47] 95.42 84.77 99.80

0.00

33.00

67.00

[47] 85.13 84.55 84.47 84.47 86.94 84.55 84.47 84.47

67.00

0.00

33.00

[47] 96.59 97.23 101.80 100.80 96.44 98.93 103.30 97.75

33.00

0.00

67.00

[47] 94.47 96.13 99.50 102.7 93.91 95.60 98.17 95.99

33.00

33.00

33.00

[47] 93.17 89.84 96.08 88.28 90.37 91.01 (continued on next page)

154

S. Sunphorka et al. / Fuel 193 (2017) 142–158

Table A2 (continued) No.

Biomass

Cellulose (wt.%)

Hemicelluloses (wt.%)

Lignin (wt.%)

46

C:H = 0.33:0.67 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) H:L = 0.67:0.33 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C:H:L = 0.17:0.67:0.17 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) Giant Leuciana b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) Hornbeam wood b = 2 K/min b = 5 K/min Empty fruit bunches; b = 10 K/min b = 50 K/min Palm shell b = 10 K/min b = 50 K/min Oriental white oak b = 5 K/min b = 20 K/min Bamboo b = 15 K/min b = 20 K/min Willow b = 20 K/min Sawdust b = 20 K/min Rice husk b = 20 K/min Palm fiber b = 5 K/min b = 15 K/min b = 20 K/min Poplar wood sawdust b = 20 K/min b = 40 K/min b = 60 K/min Bamboo b = 10 K/min Raw wood b = 30 K/min Corn cob b = 25 K/min African rice husk b = 25 K/min African Moringa b = 25 K/min African Jatropha b = 25 K/min Parinari b = 25 K/min Corn straw b = 25 K/min

33.00

67.00

0.00

47

48

49

50

51

52

53

54

55 56 57 58

59

60 61 62 63 64 65 66 67

Ea (kJ/mol)

Ref. [47]

81.37 88.31 87.65 92.02 83.03 85.53 85.73 77.85 0.00

67.00

33.00

[47] 79.56 82.08 81.57 86.29 78.68 81.94 82.84 78.76

17.00

67.00

17.00

[47] 87.89 82.76 81.57 86.29 82.80 81.46 82.84 78.76

41.88

29.93

25.46

[47] 88.52 87.85 89.09 95.00 88.80 89.16 90.07 95.33

48.90

23.30

20.10

[33] 108.30 111.10

38.30

35.30

22.10

[51] 89.07 100.40

20.80

22.70

50.70

[51] 104.40 103.40

50.40

14.30

22.80

[48] 89.05 107.80

43.30

59.72 36.08 36.06 36.69

24.60

20.06 11.18 21.34 30.51

26.20

20.22 28.66 21.16 32.80

[42] 91.03 83.85 82.11 79.71 98.26 81.42 90.59 86.02

44.74

16.73

30.72

[49] [50] [50] [5]

[5] [52]

48.00 40.49 38.26 37.34

23.60 15.74 46.59 10.07

20.60 43.77 7.16 41.08

101.40 109.10 103.20 95.29 105.50 82.85 108.70

17.92

1.85

24.96

66.71

[56]

10.08

48.83

13.96

84.50

[56]

45.41 43.82

6.42 24.39

30.11 9.03

88.37 109.30

[56] [56]

[53] [55] [56] [56]

Note: (i) C = cellulose, H = hemicelluloses, L = lignin. (ii) The difference in kinetic values obtained from the same biomass and pyrolysis condition was due to the experimental error during TGA test and TGA data fitting with an analytical model. The (2) after b represents the number of replication.

155

S. Sunphorka et al. / Fuel 193 (2017) 142–158 Table A3 The input data for calculating reaction order (n). No.

Biomass

Cellulose (wt.%)

Hemicellulose (wt.%)

Lignin (wt.%)

n (–)

Ref.

1 2 3 4 5 6 7

Hornbeam b = 2 K/min Walnut b = 2 K/min Lignin from barley b = 2 K/min Lignin from walnut b = 2 K/min Cellulose from barley b = 2 K/min Cellulose from gavotte b = 2 K/min Cellulose from hornbeam b = 2 K/min Empty fruit Bunches b = 30 K/min Palm shell b = 30 K/min Switchgrass b = 5 K/min Wheat straw b = 5 K/min Eastern Redcedar b = 5 K/min Shell b = 10 K/min Empty fruit bunches b = 10 K/min Fiber b = 10 K/min Hemicellulose from wheat straw b = 10 K/min Date seed b = 10 K/min Pine (softwood) b = 10 K/min Bamboo b = 10 K/min Rice straw b = 10 K/min Rice husk b = 10 K/min Bagasse b = 10 K/min Cotton stalk b = 10 K/min Jute b = 10 K/min Hemp b = 10 K/min Kenaf b = 10 K/min Wood b = 10 K/min Corn cobs b = 10 K/min Sugar beet b = 10 K/min Olive stones b = 10 K/min Rape extracted meal b = 10 K/min Willow b = 20 K/min Corn stalk b = 10 K/min Barley straw b = 10 K/min Timothy grass b = 10 K/min Pinewood b = 10 K/min Wood b = 10 K/min Cellulose b = 20 K/min Dairy manure b = 10 K/min Rice bran b = 10 K/min Chicken manure b = 10 K/min Bamboo b = 15 K/min Rice husk b = 10 K/min Sisal b = 10 K/min Cellulose b = 5 K/min Hemicellulose b = 5 K/min Poplar wood b = 10 K/min C: H = 0.67:0.33 b = 5 k/min b = 10 k/min b = 20 k/min b = 40 k/min b = 5 k/min (2) b = 10 k/min (2) b = 20 k/min (2) b = 40 k/min (2) C:H:L = 0.67:0.17:0.17 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C:H:L = 0.17:0.17:0.67 b = 5 K/min

48.90 47.80 0.00 0.00 100.00 100.00 100.00

23.30 22.10 0.00 0.00 0.00 0.00 0.00

20.10 25.90 100.00 100.00 0.00 0.00 0.00

1.99 1.79 1.89 2.02 2.08 2.06 2.05

[33] [33] [33] [33] [33] [33] [33]

38.30

35.30

22.10

1.37

[51]

20.80 38.46 39.18 40.30

22.70 31.04 26.30 17.90

50.70 21.40 17.17 35.90

1.35 1.51 1.49 1.64

[51] [13] [13] [13]

30.28 44.97

12.72 19.92

49.58 10.23

1.37 1.44

[57] [57]

33.21 0.00 20.00 43.00

16.58 100.00 55.00 28.00

21.79 0.00 23.00 30.50

1.42 1.34 1.20 1.86

[57] [34] [25] [35]

45.70 49.50 37.50 43.50 43.10 66.25 72.30 35.50 43.60 57.00 42.00 57.00 47.00 40.90 42.40 32.50

25.90 33.00 22.50 27.25 26.90 16.20 20.15 21.50 16.01 10.00 17.00 18.00 33.00 19.40 29.60 25.70

24.95 13.50 20.00 16.75 27.30 12.40 4.70 17.00 32.20 33.00 21.00 25.00 20.00 32.70 21.70 23.00

1.69 1.37 1.34 1.55 1.58 1.45 1.37 1.47 1.64 1.61 1.64 1.70 1.54 1.87 1.65 1.63

[35] [35] [35] [35] [35] [35] [35] [35] [27] [36] [36] [36] [36] [37] [59] [59]

31.50

27.50

24.00

1.51

[59]

39.00 22.29 100.00 9.96 16.19 7.75 43.30 28.60 73.80 100.00 0.00 43.00 67.00

34.00 23.84 0.00 23.48 17.08 19.23 24.60 28.60 11.00 0.00 100.00 28.00 33.00

12.00 47.60 0.00 10.18 7.85 5.10 26.20 24.40 9.70 0.00 0.00 25.00 0.00

1.46 1.26 2.50 1.72 2.13 1.42 1.59 1.71 2.25 2.20 1.17 1.57

[59] [60] [40] [41] [41] [41] [42] [43] [44] [45] [45] [58] [47]

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

49

50

1.20 1.19 1.24 1.37 1.19 1.21 1.27 1.35 67.00

17.00

17.00

[47] 1.20 1.21 1.34 1.35 1.19 1.20 1.37 1.38

17.00

17.00

67.00

[47] 1.13 (continued on next page)

156

S. Sunphorka et al. / Fuel 193 (2017) 142–158

Table A3 (continued) No.

51

52

53

54

55

56

57

58 59 60 61

62

Biomass b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) H:L = 0.33:0.67 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C:L = 0.67:0.33 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C:L = 0.33:0.67 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C = H = L = 0.33 b = 20 K/min b = 40 K/min b = 20 K/min (2) b = 40 K/min (2) C:H = 0.33:0.67 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) H:L = 0.67:0.33 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) C:H:L = 0.17:0.67:0.17 b = 5 K/min b = 10 K/min b = 20 K/min b = 40 K/min b = 5 K/min (2) b = 10 K/min (2) b = 20 K/min (2) b = 40 K/min (2) Giant Leuciana b = 5 K/min Hornbeam wood; b = 2 K/min Walnut wood; b = 5 K/min Empty fruit Bunches b = 10 K/min b = 50 K/min Palm shell

Cellulose (wt.%)

Hemicellulose (wt.%)

Lignin (wt.%)

n (–)

Ref.

1.15 1.15 1.14 1.13 1.15 1.10 1.13 0.00

33.00

67.00

[47] 1.12 1.10 1.11 1.14 1.11 1.10 1.11 1.14

67.00

0.00

33.00

[47] 1.18 1.20 1.29 1.30 1.18 1.21 1.32 1.27

33.00

0.00

67.00

[47] 1.14 1.19 1.26 1.36 1.14 1.16 1.24 1.27

33.00

33.00

33.00

[47] 1.22 1.30 1.21 1.26

33.00

67.00

0.00

[47] 1.19 1.18 1.22 1.30 1.19 1.17 1.19 1.36

0.00

67.00

33.00

[47] 1.15 1.13 1.20 1.24 1.16 1.14 1.14 1.23

17.00

67.00

17.00

41.88 48.90 47.80

29.93 23.30 22.10

25.46 20.10 25.90

38.30

35.30

22.10

[47] 1.13 1.13 1.20 1.24 1.15 1.14 1.14 1.23 1.33 2.02 1.98

[47] [33] [33] [51]

1.47 1.47 20.80

22.70

50.70

[51]

157

S. Sunphorka et al. / Fuel 193 (2017) 142–158 Table A3 (continued) No.

63

64 65 66 67 68 69 70

71

72 73 74 75 76 77 78

Biomass b = 10 K/min b = 50 K/min Oriental white oak b = 5 K/min b = 15 K/min b = 20 K/min Bamboo b = 5 K/min Bamboo b = 20 K/min Coconut shell b = 20 K/min Wood b = 20 K/min Rice husk b = 20 K/min Wood b = 10 K/min Palm fiber b = 15 K/min b = 20 K/min Poplar wood sawdust b = 20 K/min b = 60 K/min Raw wood b = 30 K/min Cocoa pod b = 25 K/min African rice husk b = 25 K/min African Moringa b = 25 K/min African Jatropha b = 25 K/min Parinari b = 25 K/min Rice straw b = 25 K/min

Cellulose (wt.%)

Hemicellulose (wt.%)

Lignin (wt.%)

n (–)

Ref.

1.39 1.46 50.40

14.30

22.80

43.30

24.60

26.20

10.91 41.84 48.89 36.06 39.10 36.69

33.30 21.03 25.91 21.34 28.80 30.51

55.79 39.17 25.50 21.16 12.10 32.80

[48] 2.02 1.78 1.88 1.76 1.44 1.49 1.66 1.33 1.33

[42] [49] [49] [49] [49] [51] [5]

1.61 1.64 44.74

40.49 30.41 37.34 17.92 10.08 45.41 31.84

16.73

15.74 11.97 10.07 1.85 48.83 6.42 7.29

30.72

43.77 33.96 41.08 24.96 13.96 30.11 28.39

[52] 1.62 2.03 1.41 1.53 1.61 1.59 1.46 1.81 1.58

[55] [56] [56] [56] [56] [56] [56]

Note: (i) C = cellulose, H = hemicelluloses, L = lignin. (ii) The difference in kinetic values obtained from the same biomass and pyrolysis condition was due to the experimental error during TGA test and TGA data fitting with an analytical model. The (2) after b represents the number of replication.

References [1] Chayaporn S, Sungsuk P, Sunphorka S, Kuchonthara P, Piumsomboon P, Chalermsinsuwan B. Evaluation of biomass component effect on kinetic values for biomass pyrolysis using simplex lattice design. Korean J Chem Eng 2015;32:1081–93. [2] Van de Velden M, Baeyens J, Brems A, Janssens B, Dewil R. Fundamentals, kinetics and endothermicity of the biomass pyrolysis reaction. Renew Energy 2010;35:232–42. [3] Haykiri-Acma H, Yaman S, Kucukbayrak S. Comparison of the thermal reactivities of isolated lignin and holocellulose during pyrolysis. Fuel Process Technol 2010;91:759–64. [4] Yang H, Yan R, Chen H, Lee DH, Zheng C. Characteristics of hemicellulose, cellulose and lignin pyrolysis. Fuel 2007;86:1781–8. [5] Ly HV, Kim J, Kim S-S. Pyrolysis characteristics and kinetics of palm fiber in a closed reactor. Renew Energy 2013;54:91–5. [6] Basu P. Chapter 4 - Torrefaction. In: Biomass gasification, pyrolysis and torrefaction. Boston: Academic Press; 2013. p. 87–145. [7] Jiang G, Nowakowski DJ, Bridgwater AV. A systematic study of the kinetics of lignin pyrolysis. Thermochim Acta 2010;498:61–6. [8] Peters B. Prediction of pyrolysis of pistachio shells based on its components hemicellulose, cellulose and lignin. Fuel Process Technol 2011;92:1993–8. [9] Burhenne L, Messmer J, Aicher T, Laborie M-P. The effect of the biomass components lignin, cellulose and hemicellulose on TGA and fixed bed pyrolysis. J Anal Appl Pyrol 2013;101:177–84. [10] Mangut V, Sabio E, Ganan J, Gonzalez JF, Ramiro A, Gonzalez CM, et al. Thermogravimetric study of the pyrolysis of biomass residues from tomato processing industry. J Anal Appl Pyrol 2006;87:109–15. [11] Belderok HJM. Experimental investigation and modeling of the pyrolysis of biomass. Netherlands: Department of Mechanical Engineering, Eindhoven University of Technology; 2007. [12] Couhert C, Commandre J-M, Salvador S. Is it possible to predict gas yields of any biomass after rapid pyrolysis at high temperature from its composition in cellulose, hemicellulose and lignin? Fuel 2009;88:408–17. [13] Pasangulapati V, Ramachandriya KD, Kumar A, Wilkins MR, Jones CL, Huhnke RL. Effects of cellulose, hemicellulose and lignin on thermochemical conversion characteristics of the selected biomass. Bioresour Technol 2012;114:663–9. [14] Garcia-Maraver A, Salvachua D, Martinez MJ, Diaz LF, Zamorano M. Analysis of the relation between the cellulose, hemicellulose and lignin content and the thermal behavior of residual biomass from olive trees. Waste Manage 2013;33:2245–9. [15] Couhert C, Commandré J-M, Salvador S. Failure of the component additivity rule to predict gas yields of biomass in flash pyrolysis at 950 °C. Biomass Bioenerg 2009;33:316–26. [16] Hilbers TJ, Wang Z, Pecha B, Westerhof RJM, Kersten SRA, Pelaez-Samaniego MR, et al. Cellulose-Lignin interactions during slow and fast pyrolysis. J Anal Appl Pyrol 2015;114:197–207.

[17] Basheer IA, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Meth 2000;43:3–31. [18] Buratti C, Barbanera M, Palladino D. An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks. Appl Energ 2014;120:125–32. [19] Shivakumar, Pai P Srinivasa, Rao BR Shrinivasa. Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings. Appl Energ 2011;88:2344–54. [20] Sun Y, Liu L, Wang Q, Yang X, Tu X. Pyrolysis products from industrial waste biomass based on a neural network model. J Anal Appl Pyrol 2016;120:94–102. _ Yaman S, Haykiri-Acma H. A study to predict [21] Çepeliog˘ullar Ö, Mutlu I, pyrolytic behaviors of refuse-derived fuel (RDF): artificial neural network application. J Anal Appl Pyrol 2016. [22] Conesa JA, Caballero JA, Reyes-Labarta JA. Artificial neural network for modelling thermal decompositions. J Anal Appl Pyrol 2004;71:343–52. [23] White JE, Catallo WJ, Legendre BL. Biomass pyrolysis kinetics: a comparative critical review with relevant agricultural residue case studies. J Anal Appl Pyrol 2011;91:1–33. [24] Zhu G, Zhu X, Xiao Z, Yi F. Study of cellulose pyrolysis using an in situ visualization technique and thermogravimetric analyzer. J Anal Appl Pyrol 2012;94:126–30. [25] Sait HH, Hussain A, Salema AA, Ani FN. Pyrolysis and combustion kinetics of date palm biomass using thermogravimetric analysis. Bioresour Technol 2012;118:382–9. [26] Sricharoenchaikul V, Atong D. Thermal decomposition study on Jatropha curcas L. waste using TGA and fixed bed reactor. J Anal Appl Pyrol 2009;85:155–62. [27] Lu K-M, Lee W-J, Chen W-H, Lin T-C. Thermogravimetric analysis and kinetics of co-pyrolysis of raw/torrefied wood and coal blends. Appl Energ 2013;105:57–65. [28] Munir S, Daood SS, Nimmo W, Cunliffe AM, Gibbs BM. Thermal analysis and devolatilization kinetics of cotton stalk, sugar cane bagasse and shea meal under nitrogen and air atmospheres. Bioresour Technol 2009;100:1413–8. [29] Soleymani AR, Saien J, Bayat H. Artificial neural networks developed for prediction of dye decolorization efficiency with UV/K2S2O8 process. Chem Eng J 2011;170:29–35. [30] Mohamed Ismail H, Ng HK, Queck CW, Gan S. Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends. Appl Energ 2012;92:769–77. [31] Puig-Arnavat M, Hernandez JA, Bruno JC, Coronas A. Artificial neural network models for biomass gasification in fluidized bed gasifiers. Biomass Bioenerg 2013;49:279–89. [32] Lehmann J, Joseph S. Biochar for environmental management: science and technology. London, UK: Earthscan; 2009. [33] Muller-Hagedorn M, Bockhorn H, Krebs L, Muller U. A comparative kinetic study on the pyrolysis of three different wood species. J Anal Appl Pyrol 2003;68–69:231–49.

158

S. Sunphorka et al. / Fuel 193 (2017) 142–158

[34] Peng Y, Wu S. The structural and thermal characteristics of wheat straw hemicellulose. J Anal Appl Pyrol 2010;88:134–9. [35] Yao F, Wu Q, Lei Y, Guo W, Xu Y. Thermal decomposition kinetics of natural fibers: activation energy with dynamic thermogravimetric analysis. Polym Degrad Stab 2008;93:90–8. [36] Šimkovic I, Csomorová K. Thermogravimetric analysis of agricultural residues: oxygen effect and environmental impact. J Appl Polym Sci 2006;100:1318–22. [37] Mayer ZA, Apfelbacher A, Hornung A. A comparative study on the pyrolysis of metal- and ash-enriched wood and the combustion properties of the gained char. J Anal Appl Pyrol 2012;96:196–202. [38] Naik S, Goud VV, Rout PK, Jacobson K, Dalai AK. Characterization of Canadian biomass for alternative renewable biofuel. Renew Energ 2010;35:1624–31. [39] Brebu M, Ucar S, Vasile C, Yanik J. Co-pyrolysis of pine cone with synthetic polymers. Fuel 2010;89:1911–8. [40] Wang S, Guo X, Wang K, Luo Z. Influence of the interaction of components on the pyrolysis behavior of biomass. J Anal Appl Pyrol 2011;91:183–9. [41] Xu Y, Chen B. Investigation of thermodynamic parameters in the pyrolysis conversion of biomass and manure to biochars using thermogravimetric analysis. Bioresour Technol 2013;146:485–93. [42] Oyedun AO, Gebreegziabher T, Hui CW. Mechanism and modelling of bamboo pyrolysis. Fuel Process Technol 2013;106:595–604. [43] Worasuwannarak N, Sonobe T, Tanthapanichakoon W. Pyrolysis behaviors of rice straw, rice husk, and corncob by TG-MS technique. J Anal Appl Pyrol 2007;78:265–71. [44] Martin AR, Martins MA, da Silva ORRF, Mattoso LHC. Studies on the thermal properties of sisal fiber and its constituents. Thermochim Acta 2010;506:14–9. [45] Poletto M, Zattera AJ, Forte MMC, Santana RMC. Thermal decomposition of wood: influence of wood components and cellulose crystallite size. Bioresour Technol 2012;109:148–53. [46] Odetoye TE, Onifade KR, AbuBakar MS, Titiloye JO. Thermochemical characterisation of Parinari polyandra Benth fruit shell. Ind Crop Prod 2013;44:62–6. [47] Sungsuk P, Chayaporn S. Correlations 412 of kinetic parameters in biomass pyrolysis. Bangkok:Department of Chemical Technology, Chulalonglork University; 2011.

[48] Park HJ, Park Y-K, Dong J-I, Kim J-S, Jeon J-K, Kim S-S, et al. Pyrolysis characteristics of Oriental white oak: kinetic study and fast pyrolysis in a fluidized bed with an improved reaction system. Fuel Process Technol 2009;90:186–95. [49] Chen W-H, Kuo P-C. A study on torrefaction of various biomass materials and its impact on lignocellulosic structure simulated by a thermogravimetry. Energy 2010;35:2580–6. [50] Chen W-H, Lu K-M, Tsai C-M. An experimental analysis on property and structure variations of agricultural wastes undergoing torrefaction. Appl Energ 2012;100:318–25. [51] Font R, Molto J, Galvez A, Rey MD. Kinetic study of the pyrolysis and combustion of tomato plant. J Anal Appl Pyrol 2009;85:268–75. [52] Gu X, Ma X, Li L, Liu C, Cheng K, Li Z. Pyrolysis of poplar wood sawdust by TGFTIR and Py-GC/MS. J Anal Appl Pyrol 2013;102:16–23. [53] Zeng Y, Yang X, Yu H, Zhang X, Ma F. The delignification effects of white-rot fungal pretreatment on thermal characteristics of moso bamboo. Bioresour Technol 2012;114:437–42. [54] Zeng J, Singh D, Chen S. Thermal decomposition kinetics of wheat straw treated by Phanerochaete chrysosporium. Int Biodeter Biodeg 2011;65:410–4. [55] Chen W-H, Hsu H-C, Lu K-M, Lee W-J, Lin T-C. Thermal pretreatment of wood (Lauan) block by torrefaction and its influence on the properties of the biomass. Energy 2011;36:3012–21. [56] Titiloye JO, Abu Bakar MS, Odetoye TE. Thermochemical characterisation of agricultural wastes from West Africa. Ind Crop Prod 2013;47:199–203. [57] Garcia-Nunez JA. Determination of kinetic constants and thermal modeling of pyrolysis of palm oil mill solid wastes. The University of Georgia; 2005. [58] Carrier M, Loppinet-Serani A, Denux D, Lasnier J-M, Ham-Pichavant F, Cansell F, et al. Thermogravimetric analysis as a new method to determine the lignocellulosic composition of biomass. Biomass Bioenerg 2011;35:298–307. [59] Lv G, Wu S. Analytical pyrolysis studies of corn stalk and its three main components by TG-MS and Py-GC/MS. J Anal Appl Pyrol 2012;97:11–8. [60] Wever D-AZ, Heeres HJ, Broekhuis AA. Characterization of Physic nut (Jatropha curcas L.) shells. Biomass Bioenerg 2012;37:177–87.