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Prediction of pyrolysis kinetic parameters from biomass constituents based on simplex-lattice mixture design
Prediction of Pyrolysis Kinetic Parameters from Biomass Constituents Based on Simplex-Lattice Mixture Design Panusit Sungsuk, N. Sasiporn Chayapor, Sasithorn Sunphorka, Prapan Kuchonthara, Pornpote Piumsomboon, Benjapon Chalermsinsuwan PII: DOI: Reference:
S1004-9541(16)00005-7 doi: 10.1016/j.cjche.2016.01.004 CJCHE 470
To appear in: Received date: Revised date: Accepted date:
8 February 2015 24 July 2015 31 August 2015
Please cite this article as: Panusit Sungsuk, N. Sasiporn Chayapor, Sasithorn Sunphorka, Prapan Kuchonthara, Pornpote Piumsomboon, Benjapon Chalermsinsuwan, Prediction of Pyrolysis Kinetic Parameters from Biomass Constituents Based on Simplex-Lattice Mixture Design, (2016), doi: 10.1016/j.cjche.2016.01.004
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ACCEPTED MANUSCRIPT Energy, Resources and Environmental Technology
Prediction of Pyrolysis Kinetic Parameters from Biomass Constituents Based on Simplex-lattice Mixture Design*
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Panusit SUNGSUK 1, N Sasiporn CHAYAPOR1, Sasithorn SUNPHORKA 1,2,
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Prapan KUCHONTHARA 1,3, Pornpote PIUMSOMBOON 1,3,
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Benjapon CHALERMSINSUWAN 1,3,**
1 Fuels Research Center, Department of Chemical Technology, Faculty of Science, Chulalongkorn University, 254 Phayathai Road, Patumwan, Bangkok 10330, Thailand
2 Faculty of Engineering and Architecture, Rajamangala University of Technology Tawan-ok,
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Uthenthawai Campus, 225 Phayathai Road, Bangkok 10330, Thailand
3 Center of Excellence on Petrochemical and Material Technology, Chulalongkorn University, 254
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Phayathai Road, Patumwan, Bangkok 10330, Thailand
Abstract The aim of this study is to determine the effect of the main chemical components of
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biomass: cellulose, hemicellulose and lignin, on chemical kinetics of biomass pyrolysis. The
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experiments were designed based on a simplex-lattice mixture design. The pyrolysis was observed by using a thermogravimetric analyzer. The curves obtained from the employed analytical method fit the
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experimental data (R2 > 0.9). This indicated that this method has the potential to determine the kinetic parameters such as the activation energy (Ea), frequency factor (A) and reaction order (n) for each point of the experimental design. The results obtained from the simplex-lattice mixture design
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indicated that cellulose had a significant effect on Ea and A, and the interaction between cellulose and lignin had an important effect on the reaction order, n. The proposed models were then proved to be useful for predicting pyrolysis behavior in real biomass and so could be used as a simple approximation for predicting the overall trend of chemical reaction kinetics.
Keywords biomass, pyrolysis, simplex-lattice mixture design, kinetics, modeling
Received 2015-02-08，Revised 2015-7-24， Accepted 2015-8-31. * Supported by the Grants from the Thailand Research Fund and the Commission on the Higher Education for fiscal year 2014–2016 (TRG5780205), the Grant for Development of New Faculty Staff (Ratchadaphisek Somphot Endowment Fund) of Chulalongkorn University and the Center of Excellence on Petrochemical and Materials Technology, Chulalongkorn University. ** Corresponding author. E-mail address: [email protected] 1
ACCEPTED MANUSCRIPT 1 INTRODUCTION Biomass, containing energy from the sun acquired through photosynthesis, is
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organic matter that consists of mostly carbon and hydrogen atoms. It is considered as a
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source of renewable energy that can be used in short cycle, unlike coal and petroleum. Pyrolysis is one of the most important processes to convert biomass to energy by its
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decomposition under an absence of oxygen [1]. Among the thermochemical processes for transforming biomass into energy and products, pyrolysis is the simplest to set up
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as compared to combustion and gasification [2, 3]. The products of biomass pyrolysis consist of char, tar and gas.
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To understand the mechanism and to design a suitable process for biomass pyrolysis, acknowledgement of the kinetic parameters, including the activation energy, frequency factor and reaction order, is very important. Therefore, various methods,
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analytical, differential and integral, have been developed to calculate the kinetic
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parameters using a thermogravimetric analyzer [4-12]. These methods provide slightly different values based on their assumptions. Huang et al. indicated that the analytical
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model derived by an assumption of nth-order kinetics was acquirable, representative and reliable for biomass pyrolysis [4]. Besides the calculation of kinetic parameters
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from thermogravimetric analyzer data, the prediction of kinetic parameters from biomass constituents has been explored by many studies [11, 13-16]. Gani and Naruse observed the effect of cellulose and lignin contents on pyrolysis characteristics [16]. The authors reported that higher cellulose content provided a faster pyrolysis rate. Peters attempted to describe thermal degradation of pistachio shells by a detailed reaction mechanism [14]. The results indicated that the simple model derived from kinetic values for pistachio shells pyrolysis and a discrete particle method based on biomass composition provided a good agreement between predicted values and experimental values. However, the proposed models were still quite complicated. Therefore, the development of a simplified model that can potentially predict the thermal behavior and kinetic values is still attractive. Currently, mathematical and statistical methods have been developed to examine the relationship between 2
ACCEPTED MANUSCRIPT input-output parameters. Standard mixture design methods such as the simplex-lattice mixture design (SLD) and response surface methodology (RSM) were used to evaluate the effect of three correlated factors on the desired response while other experimental
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designs are usually based on independent factors [17-20]. When using the
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simplex-lattice mixture design, the summation of three proportions must be equal to
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one. Therefore, it is suitable for applying it to a biomass system. There is literature that has reported that the RSM with the simplex design could be used to determine the effect of main factors and to interpret the interactions between the mixture components
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[18, 21]. Liu et al. used the simplex-lattice mixture design to determine the interaction between biomass components during pyrolysis [18]. The effect of components and
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their interactions on mass loss rate could be observed. However, the other kinetic values were not investigated in their work. In addition, this field of research still lacks
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studies in which this kind of model is applied.
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The aim of this study is to develop a simplified model for the prediction of kinetic values for biomass pyrolysis. Mixture design methods were applied for this purpose.
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Pure biomass constituents, including cellulose, hemicellulose and lignin and their mixtures, were used as the biomass model. Pyrolysis of pure and mixed biomass components, at the desired ratios based on the mixture design, were investigated and
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then kinetic values were examined. An analytical method was used to calculate the kinetic values from TGA data since it is simpler and gives more accurate values compared to other methods such as the Kissinger – Akahira – Sunose (KAS) and Ozawa-Flynn-Wall (OFW) methods [22]. To obtain the kinetic values, the analytical method (for reactor order equal to one and others) was fitted to thermogravimetric analysis data to calculate the kinetic values. With respect to these proposed regression models and RSM methods, the effects of pure biomass components and their interactions were identified. The calculated kinetic parameters and generated models are provided in this study and discussed in detail. This study provides useful information for simulation of biomass related processes.
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ACCEPTED MANUSCRIPT 2 EXPERIMENTAL 2.1 Materials
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Pure biomass components, including cellulose (-cellulose, catalogue number
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C8002), hemicellulose (xylan, catalogue number X4252) and lignin (organosolv,
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catalogue number 371017), were purchased from Sigma-Aldrich and used to develop synthetic biomass. Leucaena Leucocephala [mass ratio of cellulose: hemicellulose: lignin = 43.06: 30.77: 26.17 (dry, ash-free basis)] was used as a biomass model.
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Proximate analysis was performed by ASTM E870 – 82. All natural samples were dried at 110 oC for 24 h. The dried Leucaena Leucocephala was then ground by a
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biomass grinder and filtered into particles at the size of 150-250 micron.
2.2 Thermogravimetric analysis (TGA)
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Pyrolysis of synthetic biomass and natural biomass was carried out by using a
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thermogravimetric/ differential thermal analyzer (Mettler Toledo TG Analyzer 851e model) under nitrogen atmosphere. Approximately 3.0 mg of sample was placed into
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an aluminum pan. The flow rate of nitrogen gas into the cell was 50 ml·min-1. The sample was heated from 30 to 1000 oC at linear heating rates of 5, 10, 20 and 40 oC
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min-1, respectively.
2.3 Derivation of kinetic parameters from thermogravimetric analysis data The TGA curves represent the overall weight loss rate of biomass pyrolysis. Therefore, they were used to determine the kinetic parameters of synthesized biomass pyrolysis in this study. For the non-isothermal system, the rate of decomposition (d/dt) combined with the Arrhenius equation was expressed by Eq. (1).
d n n E k 1 A exp a 1 dt RT
(1)
where α is the fraction of solid materials decomposed at any time t which is defined using Eq. (2), n is the reaction order, k is the rate constant given by the 4
ACCEPTED MANUSCRIPT Arrhenius equation, A is the frequency factor (s-1), Ea is the activation energy (kJ·kmol-1), R is a gas constant (8.314 kJ·kmol-1·K-1) and T is the reaction temperature (K).
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W0 W W0 Wash
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(2)
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where W0 is the original weight, W is the weight at any time t and Wash is the ash content in the sample.
d A n E exp 1 dT RT
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For linear heating rate, β = dT/dt, Eq. (1) becomes
(3)
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An analytical method is the model for a single step decomposition reaction. It can be solved by integrating Eq. (3). Its integrated form of Eq. (3) for 1st order and nth order
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of reaction can be expressed as shown in Eqs. (4) and (5), respectively.
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For 1st order of reaction
ART 2 2 RT 1 Ea Ea
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1 exp
Ea exp RT
(4)
For nth order of reaction
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ART 2 2 RT 1 1 n 1 1 E Ea a
1
Ea exp RT
1n
(5)
For a case of nth order of reaction, n was calculated by the Kissinger index of
shape equation. The shape index is defined as the absolute value of the ratio of the slope of tangents to the curve at inflection points of differential thermal analysis (DTA) curves [23, 24]. To calculate the other kinetic parameters, including Ea and A, the TGA curves were fitted with analytical models [Eqs. (4) and (5)] by means of maximizing the regression coefficient (R2). Then, the model which shows the better fit to the experimental data will be chosen to develop the correlation between biomass compositions and kinetic parameters.
2.4 Experimental design and statistical analysis 5
ACCEPTED MANUSCRIPT In this study, an analysis of variance (ANOVA) and response surface methodology (RSM) based on SLD was used to evaluate the effect of mass fraction of cellulose (X1), mass fraction of hemicellulose (X2) and mass fraction of lignin (X3) on each kinetic
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parameter. The SLD is suitable for three dependent factors when the fraction of three
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components is one. Three factors are main components of biomass, including cellulose,
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hemicellulose and lignin. Based on SLD, the samples were prepared by mixing the three components at different ratios with the summation of the proportions (X1 + X2 + X3) one. The sample codes and mass fraction of each experimental design point as 13
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combinations are presented in Table 1. The three experimental points were pure component treatments; the six experimental points were two-component mixtures, and
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the four experimental points were three-component mixtures. For each mixture, the experiments were performed at four linear heating rates: 5, 10, 20 and 40 oC·min-1 with
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duplicates.
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In an ANOVA, the p-value was used to test the statistical hypothesis. The chosen significance level was 5% which corresponded to p-value = 0.05. The p-values below
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0.05 indicated the statistical significance of the factors. The general regression model is expressed in terms of polynomial equation [Eq. (6)]. (6)
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Y= a1X1 + a2X2 + a3X3 + a12X1 X2 + a13X1 X3 + a23X2 X3
where Y is an estimated response and a1, a2, a3, a12, a13 and a23 are constant
coefficients for linear and non-linear terms calculated from the experimental results using the ANOVA method [17, 19]. Both direction and magnitude of the constant coefficient indicate the effect and impact of each term on the response. The ternary contour plots for estimated Ea, A and n were also generated from the proposed regression models. The plots were superimposed to observe the influence of biomass composition on each kinetic parameter. To check the accuracy of the model, the pyrolysis of real biomass, Leucaena Leucocephala, was also performed. The kinetic parameters of pyrolysis of the real biomass would be predicted from the proposed regression models. Then, the conversion curve obtained from predicted kinetic values was compared to that obtained 6
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Mass fraction
0.67
3
0.17
4
0.00
5
0.67
6
1.00
7
0.33
8
0.33
9
0.33
13
0.17
0.17
0.67
0.33
0.67
0.00
0.33
0.00
0.00
0.00
0.67
0.33
0.33
0.67
0.00
0.67
0.33
0.00
0.00
1.00
0.00
1.00
0.00
0.17
0.67
0.17
0.00
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12
0.17
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10 11
0.00
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2
Lignin
0.33
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0.67
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1
Hemicellulose
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Cellulose
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Sample code
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Table 1. Mass fraction of biomass components based on SLD.
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3 RESULTS AND DISCUSSION 3.1 Pyrolysis of pure component Figs. 1(a), 1(b) and 1(c) show the TGA and DTA curves for pyrolysis of cellulose, hemicellulose and lignin, respectively, with heating rates of 5, 10, 20 and 40 oC·min-1. They illustrate the different trends of the decomposition profile for three components. As can be seen from the TGA curves, cellulose started to degrade at the highest temperature (~270 oC) when compared to the other components. This is due to the crystalline micro-fibril arrangement of cellulose which makes it resistant to thermal decomposition [25]. Moreover, the cellulose is composed of only one simple repeating unit, cellubiose. Therefore, it dramatically decomposes within a short temperature range. This behavior provided only one DTA peak which exhibited the highest 7
ACCEPTED MANUSCRIPT magnitude among the DTA curve of all components, resulting in the expected highest Ea. Hemicellulose is composed of several monosaccharides, including glucose,
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xylose, mannose, galactose and arabinose. Therefore, it has more amorphous structure
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than cellulose [26]. It started to decompose at a lower temperature than cellulose and
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then slowly decomposed under a broad temperature range. The DTA curve of hemicellulose pyrolysis had two main peaks because of its different main structures, glucose and xylan.
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Lignin has a very complex structure and consists of three common phenylpropane structures: p-hydroxyphenyl, syringyl, and guaiacyl units. It is thus the most difficult
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one to decompose. Its decomposition happened slowly at a lower temperature and finished at the highest temperature. In a comparison of DTA curves of hemicellulose
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and lignin, DTA obtained from lignin pyrolysis had the lowest magnitude with the
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widest range. Moreover, the amount of remaining solid from lignin pyrolysis was higher than those for hemicellulose and cellulose due to its largely complex structure
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that mostly contains aromatics.
Effect of heating rate is also demonstrated in Fig. 1. It can be seen that the TGA curves and DTA peaks shifted towards higher temperature with an increase in the
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heating rate. This was because of the thermal lag at a higher heating rate at which time the difference of temperature between sample and furnace was greater than those at lower heating rates, resulting in slower decomposition [27]. These results are correspondent with some of the published literature [28, 29].
(a) 8
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(b)
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(c)
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Figure 1 TG/DTA curves of cellulose (a), hemicellulose (b) and lignin (c)
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at different heating rates.
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3.2 Pyrolysis of mixed components The pyrolysis of two-component mixtures and three-component mixtures were explored. The TGA and DTA curves of pyrolysis of mixtures at 5 oC·min-1 are shown in
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Fig. 2. Fig. 2(a) presents TGA and DTA curves of cellulose and hemicellulose mixtures. It can be seen that two main peaks of DTA curves appeared. The first peak might belong to the decomposition of the hemicellulose proportion since it appeared at the same position as can be observed in a case of pyrolysis of pure hemicellulose. The second peak belongs to the cellulose proportion for the same reason. Moreover, the quantity of the components are demonstrated by the means of peak magnitude. For example, the magnitude of the first peak decreased with the decrease of the hemicellulose fraction. Fig. 2(b) presents the TGA and DTA curves of cellulose and lignin mixtures. The DTA curve had only one main peak and its magnitude agreed with the mass fraction of cellulose. It was probably because all cellulose proportion decomposed within a short 9
ACCEPTED MANUSCRIPT temperature range as mentioned above. Therefore, the DTA curve of cellulose pyrolysis became dominant. The same phenomenon was found in a case of hemicellulose and lignin mixtures [Fig. 2(c)] which showed that the DTA curve had two main peaks at the
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same positions as the DTA curve obtained from pure hemicellulose pyrolysis.
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In the case of three-component mixtures, Fig. 2(d) shows TGA and DTA curves of
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the pyrolysis of mixtures. It can be seen that the DTA curves have two or three main peaks depending on the mass fraction of cellulose and hemicellulose. For the results of other heating rates, the TGA and DTA curves showed the same trend as Fig. 2, but the
(a)
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curves shifted forward with an increase in heat rates (the results not shown here).
(b)
(c)
10
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(d)
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Fig. 2 TG/DTA curves of synthesized biomass: cellulose: hemicellulose (a), cellulose: lignin (b), hemicellulose: lignin (c) and cellulose: hemicellulose: lignin (d)
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at different ratios.
3.3 The selection of analytical method
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Before all kinetic parameters were calculated, a suitable analytical method had to be selected from two different cases: for n=1 and n1. Fig. 3 illustrates the relationship
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between conversion and temperature of the three-component mixture from TGA and
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two analytical methods at 5oC·min-1. It shows that the curves from the analytical method for n≠1 was closed to the curve obtained from TGA (R2 = 0.96) when
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compared to the curves obtained from the analytical method for n=1. Therefore, this method (n≠1) is accurate enough and was thus chosen for the further analysis. From the TGA and DTA data, the activation energy, frequency factor and reaction
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order of each composition were calculated by the selected method as described in Section 2.3. Then, the effect of component proportion on each kinetic parameter and mathematical relation of biomass constituents and kinetic parameters were identified as described below.
Figure 3 The curve obtained from experimental data (cellulose: hemicellulose: lignin = 11
ACCEPTED MANUSCRIPT 0.67:0.17:0.17), the fitted curve obtained from analytical method for n = 1 and the fitted curve obtained from analytical method for n 1.
3.4 Calculated kinetic parameters
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The TGA curves were fitted according to the analytical methods for n=1 [Eq. (4)] and n1 [Eq. (5)]. The kinetic parameters for both cases were then calculated as
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presented in Table 2. Among pure components (Sample nos. 6, 11 and 12), pyrolysis of cellulose showed the highest Ea and A as described in Section 3.1. However, the trend of kinetic parameters obtained from other synthesized biomass could not be clearly
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observed. This might be due to the interaction between biomass components.
Table 2. Calculated kinetic parameters of biomass pyrolysis from analytical methods (for reaction order = 1 and reaction order 1). n1
n=1
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Sample code
A /s-1
n
Ea /kJ·mol-1
A /s-1
18.18
9.78×105
9..4
47..9
6.41×105
881.47
6.43×108
...5
15.7.
6.03×104
.9.94
3.62×104
..47
879..5
1.70×106
4
.9.9.
4.00×104
5.99
871.11
2.81×106
5
89...1
1.10×109
..49
41.11
2.53×107
6
8.4.81
2.63×1013
8.11
891.81
3.67×1012
7
88...4
6.38×108
..71
871.99
1.55×107
8
14.79
2.07×106
5.91
871.48
2.13×106
9
19.51
6.80×107
..11
879.91
1.79×107
10
.8.44
1.14×105
5.9.
54.11
6.67×105
11
...95
3.62×104
5.79
871.99
4.40×105
12
5..19
1.13×105
..5.
14.45
1.79×106
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Ea /kJ·mol-1
2
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3
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1
12
ACCEPTED MANUSCRIPT 3.79×107
19.7.
13
5.11
4...9
1.79×106
Average data from eight replicates
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Moreover, the trend of kinetic parameters obtained from both analytical methods
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was different because the changes in reaction order influenced the changes of other
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parameters during the calculation. From the results obtained from Section 3.3, an analytical model for n1 showed a better fit to the experimental data. Therefore, the
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kinetic parameters calculated from this model were next used in the statistical analysis.
3.5 Statistical analysis and modeling
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To identify the effect of each biomass components on kinetic parameters and generate a statistical model for prediction of kinetic parameters, the RSM was
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employed. An analysis of variance (ANOVA) for Ea, A and n are shown in Table 3(a),
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3(b) and 3(c), respectively. The p-value shows the probability of kinetic parameters affected by the main composition during biomass pyrolysis. The terms which have
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p-value less than 0.05 have an important effect on the biomass pyrolysis. The ANOVA indicated that all independent biomass components: cellulose (X1), hemicellulose (X2) and lignin (X3), were the most significant factors affecting Ea and A. In case of n,
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independent components and two-level interaction of components, including X1X2, X1X3 and X2X3, were significant factors. To verify the ANOVA, the plots of normal probability of residuals, residuals versus predicted value and residuals versus effect for Ea, A and n were generated and are shown in Figs. A1, A2 and A3, respectively. The normal probability plots for all parameters show a straight line, indicating normal distribution of errors. The plots of residuals versus predicted value and residuals versus effect show that the residuals have no pattern. The validation revealed that there were no problems in the analysis. The final mathematical models associated to the responses in terms of actual factors [Eqs. (6)] are shown below.
Ea = 153.35X1 + 74.94X2 + 75.77X3
(7) 13
ACCEPTED MANUSCRIPT lg A = 11.20X1 + 5.39X2 + 5.15X3
(8)
n = 2.11X1 + 5.70X2 + 5.71X3 + 6.87X1X2 + 11.49X1X3 + 6.45X2X3
(9)
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From these regression models, the direction and magnitude of the constant
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coefficient of each term demonstrated that cellulose proportion had the most effect on
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Ea and A. An increase in the mass fraction of cellulose increased Ea and A, as a simple repeating unit of cellulose can be dramatically decomposed within a short time and temperature range. Therefore, an increase in this fraction leads to the simple
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decomposition of biomass at appropriate temperature, resulting in higher Ea and A. For n, interaction between the mass fractions of cellulose (X1) and lignin (X3) had the most
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effect.
The ternary plots were generated by the regression models. Fig. 4 shows the
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ternary contour plots indicating the effects of the interaction between main components
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and Ea [Fig. 4(a)], A [Fig. 4(b)] and n [Fig. 4(c)]. The effects of the components and interactions on the kinetic parameters can observed by these plots. These results
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provide useful information to predict the kinetic parameters of biomass pyrolysis at
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different compositions.
Table 3(a). The analysis of variance (ANOVA) of activation energy Source
Sum of Squares
DF
Mean Square
F Value
p-value > F
Model
7419.70
6
1236.62
7.78
0.01a
Linear Mixture
6401.55
2
3200.77
20.14
0.01a
X1X2
850.23
1
850.23
5.35
0.06
X1X3
42.46
1
42.46
0.27
0.62
X2X3
82.20
1
82.2
0.52
0.49
X1X2X3
43.26
1
43.26
0.27
0.62
Residual
953.45
6
158.91
Total
8373.15
12 14
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Table 3(b). The analysis of variance (ANOVA) of frequency factor
Sum of Squares
DF
Mean Square
F Value
p-value > F
Model
45.00
2
22.50
8.69
0.01a
Linear Mixture
45.00
2
22.50
8.69
0.01a
Residual
25.90
10
2.59
Total
70.89
12
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Source
Sum of Squares
DF
Mean Square
F Value
p-value > F
Model
22.01
6
3.67
15.16
0.01a
Linear Mixture
9.99
2
4.99
20.63
0.01a
X1X2
2.39
1
2.39
9.87
0.02a
X1X3
6.69
1
6.69
27.65
0.01a
X2X3
2.11
1
2.11
8.70
0.03a
0.83
1
0.83
3.43
0.11
1.45
6
0.24
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Residual Total
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Source
X1X2X3
23.46
12
Significant F-values at the 95% confidence level (p-value 0.05). DF = Degrees of freedom
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a
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Table 3(c). The analysis of variance (ANOVA) of reaction order
(a)
15
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(b)
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(c)
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Figure 4 Ternary contour plots of predicted activation energy (a), logarithm of frequency factor
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(b) and reaction order (c).
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3.6 Application to real biomass The biomass sample, Leucaena Leucocephala, with known composition was used
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to test the accuracy of models. Its composition was substituted into Eqs. (7) – (9) in order to calculate the kinetic parameters. Then, the calculated parameters were substituted into Eq. (5). The relationship between temperature and conversion was plotted and compared to the TGA curve as shown in Fig. 5. The R2 value was close to unity (0.98) which demonstrates a good agreement of the generated model and experimental data. In addition, these models were applied to calculate the kinetic values of other biomass pyrolysis. The results and R2 values are presented in Table 4. As high R2 values were obtained, the analytical method for n1 and statistical analysis have the potential to identify the correlation between biomass components and kinetic values for biomass pyrolysis.
Table 4. Predicted kinetic values of different biomass 16
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Cellulose
Hemicellulose
Lignin
R2
Ref.
Pine cone
34.35
39.50
26.16
0.87
[30]
Rice straw
35.56
39.67
24.78
0.90
[31]
Parinari fruit shell
55.43
7.81
36.75
0.83
[32]
Poplar wood
44.79
29.17
26.04
0.94
Mongolia oak tree
36.69
30.51
32.80
0.89
[34]
Launa wood
40.49
15.74
Wheat straw
41.00
35.00
Corn cob
41.58
50.64
Sugarcane
53.17
27.78
Corn straw
56.73
31.58
[33]
43.77
0.92
[35]
24.00
0.90
[36]
7.78
0.93
[37]
19.05
0.96
[37]
11.69
0.98
[37]
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Biomass
However, while the error was acceptable with respect to R2 value, the difference
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between experimental and predicted data was detected at moderate temperature (Fig.
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5). This is due to the assumption of the kinetic model. In addition, xylan and
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organosolv lignin were used for the hemicellulose and lignin model, respectively, since these forms of biomass components are largely found in nature. Still, while their behaviors are different from real hemicellulose and lignin, the obtained model can be
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used as a simple approximation for predicting the overall trend of chemical reaction kinetics.
To generate a more precise conversion curve, other solid-state kinetic reactions,
including nucleation, geometrical contraction and diffusion, should be applied into the kinetic models. In real biomass, the interaction between all components, particles-volatiles and the effect of alkali and alkaline earth metal species can be very strong [15, 38]. However, the effect of ash on kinetic parameters is not important comparing to the used reactive species. Thus, besides applying other solid-state kinetic reactions, the terms for those involved the interaction should be included for more accuracy.
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Figure 5 The conversion curves obtained from experimental data and predicted data of pyrolysis of Leucaena Leucocephala at 20 oC·min-1; R2 = 0.98.
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4 CONCLUSIONS
The relationship between biomass constituent and kinetic parameters for biomass
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pyrolysis was determined. Response surface methodology and simplex-lattice mixture design were used for this purpose. The results show that a model obtained from the
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analytical method (for n1) can be appropriately used to calculate the activation energy,
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frequency factor reaction order from thermogravimetric analysis data that provided a high regression coefficient. From statistical analysis, cellulose has the most significant
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effect on the activation energy and the frequency factor while interaction between cellulose and lignin have the most effect on the reaction order. The generated regression models for each kinetic parameter were used to produce ternary contour
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plots and determine the kinetic parameters. Then, Leucaena Leucocephala was used as an example for the accuracy test of these models. The results show that the calculated kinetic values provide a very acceptable thermogravimetric analysis curve compared to the curve obtained from the experiment. It is expected that this work provides useful information for estimating the kinetic parameters for other biomass pyrolysis and designing suitable process operating conditions.
ACKNOWLEDGEMENTS The authors would like to express their thanks to the Graduate School, Chulalongkorn University for partial financial support. The authors also thank the
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editing.
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Chalermsinsuwan, B., Evaluation of biomass component effect on kinetic values for biomass pyrolysis using simplex lattice design, Korean J. Chem. Eng. Accepted menuscript (2014). [23] Lin, C.-P., Chang, Y.-M., Gupta, J. P., Shu, C.-M., Comparisons of TGA and DSC approaches to evaluate nitrocellulose thermal degradation energy and stabilizer efficiencies, Process Saf.
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[32] Odetoye, T. E., Onifade, K. R., AbuBakar, M. S., Titiloye, J. O., Thermochemical characterisation of Parinari polyandra Benth fruit shell, Ind. Crop. Prod., 44 (2013) 62-66. [33] Carrier, M., Loppinet-Serani, A., Denux, D., Lasnier, J.-M., Ham-Pichavant, F., Cansell, F., et al.,
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Thermogravimetric analysis as a new method to determine the lignocellulosic composition of biomass, Biomass Bioenerg. 35 (2011) 298-307.
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[34] Li, D., Chen, L., Zhang, X., Ye, N., Xing, F., Pyrolytic characteristics and kinetic studies of three kinds of red algae, Biomass Bioenerg. 35 (2011) 1765-1772. [35] Chen, W.-H., Hsu, H.-C., Lu, K.-M., Lee, W.-J., Lin, T.-C., Thermal pretreatment of wood
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(2011) 3012-3021.
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(Lauan) block by torrefaction and its influence on the properties of the biomass, Energy 36
[36] Zeng, J., Singh, D., Chen, S., Thermal decomposition kinetics of wheat straw treated by
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Phanerochaete chrysosporium, Int. Biodeter. Biodegr. 65 (2011) 410-414. [37] Titiloye, J. O., Abu Bakar, M. S., Odetoye, T. E., Thermochemical characterisation of agricultural wastes from West Africa, Ind. Crop. Prod. 47 (2013) 199-203. [38] Sonoyama, N., Hayashi, J.-i., Characterisation of coal and biomass based on kinetic parameter
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distributions for pyrolysis, Fuel 114 (2013) 206-15.
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Figure A1 Normal probability plot of residuals (a), residual vs predicted values (b) and residual vs factor (c) for the prediction of activation energy.
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Figure A2 Normal probability plot of residuals (a), residual vs predicted values (b) and residual vs factor (c) for the prediction of logarithm of frequency factor.
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Fig. A3. Normal probability plot of residuals (a), residual vs. predicted values (b) and residual vs
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Ternary contour plots of predicted activation energy (a), logarithm of frequency factor (b) and
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S1004-9541(16)00005-7 doi: 10.1016/j.cjche.2016.01.004 CJCHE 470
To appear in: Received date: Revised date: Accepted date:
8 February 2015 24 July 2015 31 August 2015
Please cite this article as: Panusit Sungsuk, N. Sasiporn Chayapor, Sasithorn Sunphorka, Prapan Kuchonthara, Pornpote Piumsomboon, Benjapon Chalermsinsuwan, Prediction of Pyrolysis Kinetic Parameters from Biomass Constituents Based on Simplex-Lattice Mixture Design, (2016), doi: 10.1016/j.cjche.2016.01.004
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ACCEPTED MANUSCRIPT Energy, Resources and Environmental Technology
Prediction of Pyrolysis Kinetic Parameters from Biomass Constituents Based on Simplex-lattice Mixture Design*
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Panusit SUNGSUK 1, N Sasiporn CHAYAPOR1, Sasithorn SUNPHORKA 1,2,
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Prapan KUCHONTHARA 1,3, Pornpote PIUMSOMBOON 1,3,
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Benjapon CHALERMSINSUWAN 1,3,**
1 Fuels Research Center, Department of Chemical Technology, Faculty of Science, Chulalongkorn University, 254 Phayathai Road, Patumwan, Bangkok 10330, Thailand
2 Faculty of Engineering and Architecture, Rajamangala University of Technology Tawan-ok,
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Uthenthawai Campus, 225 Phayathai Road, Bangkok 10330, Thailand
3 Center of Excellence on Petrochemical and Material Technology, Chulalongkorn University, 254
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Phayathai Road, Patumwan, Bangkok 10330, Thailand
Abstract The aim of this study is to determine the effect of the main chemical components of
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biomass: cellulose, hemicellulose and lignin, on chemical kinetics of biomass pyrolysis. The
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experiments were designed based on a simplex-lattice mixture design. The pyrolysis was observed by using a thermogravimetric analyzer. The curves obtained from the employed analytical method fit the
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experimental data (R2 > 0.9). This indicated that this method has the potential to determine the kinetic parameters such as the activation energy (Ea), frequency factor (A) and reaction order (n) for each point of the experimental design. The results obtained from the simplex-lattice mixture design
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indicated that cellulose had a significant effect on Ea and A, and the interaction between cellulose and lignin had an important effect on the reaction order, n. The proposed models were then proved to be useful for predicting pyrolysis behavior in real biomass and so could be used as a simple approximation for predicting the overall trend of chemical reaction kinetics.
Keywords biomass, pyrolysis, simplex-lattice mixture design, kinetics, modeling
Received 2015-02-08，Revised 2015-7-24， Accepted 2015-8-31. * Supported by the Grants from the Thailand Research Fund and the Commission on the Higher Education for fiscal year 2014–2016 (TRG5780205), the Grant for Development of New Faculty Staff (Ratchadaphisek Somphot Endowment Fund) of Chulalongkorn University and the Center of Excellence on Petrochemical and Materials Technology, Chulalongkorn University. ** Corresponding author. E-mail address: [email protected] 1
ACCEPTED MANUSCRIPT 1 INTRODUCTION Biomass, containing energy from the sun acquired through photosynthesis, is
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organic matter that consists of mostly carbon and hydrogen atoms. It is considered as a
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source of renewable energy that can be used in short cycle, unlike coal and petroleum. Pyrolysis is one of the most important processes to convert biomass to energy by its
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decomposition under an absence of oxygen [1]. Among the thermochemical processes for transforming biomass into energy and products, pyrolysis is the simplest to set up
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as compared to combustion and gasification [2, 3]. The products of biomass pyrolysis consist of char, tar and gas.
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To understand the mechanism and to design a suitable process for biomass pyrolysis, acknowledgement of the kinetic parameters, including the activation energy, frequency factor and reaction order, is very important. Therefore, various methods,
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analytical, differential and integral, have been developed to calculate the kinetic
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parameters using a thermogravimetric analyzer [4-12]. These methods provide slightly different values based on their assumptions. Huang et al. indicated that the analytical
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model derived by an assumption of nth-order kinetics was acquirable, representative and reliable for biomass pyrolysis [4]. Besides the calculation of kinetic parameters
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from thermogravimetric analyzer data, the prediction of kinetic parameters from biomass constituents has been explored by many studies [11, 13-16]. Gani and Naruse observed the effect of cellulose and lignin contents on pyrolysis characteristics [16]. The authors reported that higher cellulose content provided a faster pyrolysis rate. Peters attempted to describe thermal degradation of pistachio shells by a detailed reaction mechanism [14]. The results indicated that the simple model derived from kinetic values for pistachio shells pyrolysis and a discrete particle method based on biomass composition provided a good agreement between predicted values and experimental values. However, the proposed models were still quite complicated. Therefore, the development of a simplified model that can potentially predict the thermal behavior and kinetic values is still attractive. Currently, mathematical and statistical methods have been developed to examine the relationship between 2
ACCEPTED MANUSCRIPT input-output parameters. Standard mixture design methods such as the simplex-lattice mixture design (SLD) and response surface methodology (RSM) were used to evaluate the effect of three correlated factors on the desired response while other experimental
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designs are usually based on independent factors [17-20]. When using the
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simplex-lattice mixture design, the summation of three proportions must be equal to
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one. Therefore, it is suitable for applying it to a biomass system. There is literature that has reported that the RSM with the simplex design could be used to determine the effect of main factors and to interpret the interactions between the mixture components
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[18, 21]. Liu et al. used the simplex-lattice mixture design to determine the interaction between biomass components during pyrolysis [18]. The effect of components and
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their interactions on mass loss rate could be observed. However, the other kinetic values were not investigated in their work. In addition, this field of research still lacks
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studies in which this kind of model is applied.
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The aim of this study is to develop a simplified model for the prediction of kinetic values for biomass pyrolysis. Mixture design methods were applied for this purpose.
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Pure biomass constituents, including cellulose, hemicellulose and lignin and their mixtures, were used as the biomass model. Pyrolysis of pure and mixed biomass components, at the desired ratios based on the mixture design, were investigated and
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then kinetic values were examined. An analytical method was used to calculate the kinetic values from TGA data since it is simpler and gives more accurate values compared to other methods such as the Kissinger – Akahira – Sunose (KAS) and Ozawa-Flynn-Wall (OFW) methods [22]. To obtain the kinetic values, the analytical method (for reactor order equal to one and others) was fitted to thermogravimetric analysis data to calculate the kinetic values. With respect to these proposed regression models and RSM methods, the effects of pure biomass components and their interactions were identified. The calculated kinetic parameters and generated models are provided in this study and discussed in detail. This study provides useful information for simulation of biomass related processes.
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ACCEPTED MANUSCRIPT 2 EXPERIMENTAL 2.1 Materials
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Pure biomass components, including cellulose (-cellulose, catalogue number
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C8002), hemicellulose (xylan, catalogue number X4252) and lignin (organosolv,
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catalogue number 371017), were purchased from Sigma-Aldrich and used to develop synthetic biomass. Leucaena Leucocephala [mass ratio of cellulose: hemicellulose: lignin = 43.06: 30.77: 26.17 (dry, ash-free basis)] was used as a biomass model.
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Proximate analysis was performed by ASTM E870 – 82. All natural samples were dried at 110 oC for 24 h. The dried Leucaena Leucocephala was then ground by a
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biomass grinder and filtered into particles at the size of 150-250 micron.
2.2 Thermogravimetric analysis (TGA)
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Pyrolysis of synthetic biomass and natural biomass was carried out by using a
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thermogravimetric/ differential thermal analyzer (Mettler Toledo TG Analyzer 851e model) under nitrogen atmosphere. Approximately 3.0 mg of sample was placed into
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an aluminum pan. The flow rate of nitrogen gas into the cell was 50 ml·min-1. The sample was heated from 30 to 1000 oC at linear heating rates of 5, 10, 20 and 40 oC
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min-1, respectively.
2.3 Derivation of kinetic parameters from thermogravimetric analysis data The TGA curves represent the overall weight loss rate of biomass pyrolysis. Therefore, they were used to determine the kinetic parameters of synthesized biomass pyrolysis in this study. For the non-isothermal system, the rate of decomposition (d/dt) combined with the Arrhenius equation was expressed by Eq. (1).
d n n E k 1 A exp a 1 dt RT
(1)
where α is the fraction of solid materials decomposed at any time t which is defined using Eq. (2), n is the reaction order, k is the rate constant given by the 4
ACCEPTED MANUSCRIPT Arrhenius equation, A is the frequency factor (s-1), Ea is the activation energy (kJ·kmol-1), R is a gas constant (8.314 kJ·kmol-1·K-1) and T is the reaction temperature (K).
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W0 W W0 Wash
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(2)
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where W0 is the original weight, W is the weight at any time t and Wash is the ash content in the sample.
d A n E exp 1 dT RT
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For linear heating rate, β = dT/dt, Eq. (1) becomes
(3)
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An analytical method is the model for a single step decomposition reaction. It can be solved by integrating Eq. (3). Its integrated form of Eq. (3) for 1st order and nth order
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of reaction can be expressed as shown in Eqs. (4) and (5), respectively.
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For 1st order of reaction
ART 2 2 RT 1 Ea Ea
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1 exp
Ea exp RT
(4)
For nth order of reaction
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ART 2 2 RT 1 1 n 1 1 E Ea a
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Ea exp RT
1n
(5)
For a case of nth order of reaction, n was calculated by the Kissinger index of
shape equation. The shape index is defined as the absolute value of the ratio of the slope of tangents to the curve at inflection points of differential thermal analysis (DTA) curves [23, 24]. To calculate the other kinetic parameters, including Ea and A, the TGA curves were fitted with analytical models [Eqs. (4) and (5)] by means of maximizing the regression coefficient (R2). Then, the model which shows the better fit to the experimental data will be chosen to develop the correlation between biomass compositions and kinetic parameters.
2.4 Experimental design and statistical analysis 5
ACCEPTED MANUSCRIPT In this study, an analysis of variance (ANOVA) and response surface methodology (RSM) based on SLD was used to evaluate the effect of mass fraction of cellulose (X1), mass fraction of hemicellulose (X2) and mass fraction of lignin (X3) on each kinetic
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parameter. The SLD is suitable for three dependent factors when the fraction of three
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components is one. Three factors are main components of biomass, including cellulose,
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hemicellulose and lignin. Based on SLD, the samples were prepared by mixing the three components at different ratios with the summation of the proportions (X1 + X2 + X3) one. The sample codes and mass fraction of each experimental design point as 13
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combinations are presented in Table 1. The three experimental points were pure component treatments; the six experimental points were two-component mixtures, and
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the four experimental points were three-component mixtures. For each mixture, the experiments were performed at four linear heating rates: 5, 10, 20 and 40 oC·min-1 with
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duplicates.
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In an ANOVA, the p-value was used to test the statistical hypothesis. The chosen significance level was 5% which corresponded to p-value = 0.05. The p-values below
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0.05 indicated the statistical significance of the factors. The general regression model is expressed in terms of polynomial equation [Eq. (6)]. (6)
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Y= a1X1 + a2X2 + a3X3 + a12X1 X2 + a13X1 X3 + a23X2 X3
where Y is an estimated response and a1, a2, a3, a12, a13 and a23 are constant
coefficients for linear and non-linear terms calculated from the experimental results using the ANOVA method [17, 19]. Both direction and magnitude of the constant coefficient indicate the effect and impact of each term on the response. The ternary contour plots for estimated Ea, A and n were also generated from the proposed regression models. The plots were superimposed to observe the influence of biomass composition on each kinetic parameter. To check the accuracy of the model, the pyrolysis of real biomass, Leucaena Leucocephala, was also performed. The kinetic parameters of pyrolysis of the real biomass would be predicted from the proposed regression models. Then, the conversion curve obtained from predicted kinetic values was compared to that obtained 6
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Mass fraction
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0.33
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0.00
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0.33
0.67
0.00
0.67
0.33
0.00
0.00
1.00
0.00
1.00
0.00
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Cellulose
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Sample code
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Table 1. Mass fraction of biomass components based on SLD.
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3 RESULTS AND DISCUSSION 3.1 Pyrolysis of pure component Figs. 1(a), 1(b) and 1(c) show the TGA and DTA curves for pyrolysis of cellulose, hemicellulose and lignin, respectively, with heating rates of 5, 10, 20 and 40 oC·min-1. They illustrate the different trends of the decomposition profile for three components. As can be seen from the TGA curves, cellulose started to degrade at the highest temperature (~270 oC) when compared to the other components. This is due to the crystalline micro-fibril arrangement of cellulose which makes it resistant to thermal decomposition [25]. Moreover, the cellulose is composed of only one simple repeating unit, cellubiose. Therefore, it dramatically decomposes within a short temperature range. This behavior provided only one DTA peak which exhibited the highest 7
ACCEPTED MANUSCRIPT magnitude among the DTA curve of all components, resulting in the expected highest Ea. Hemicellulose is composed of several monosaccharides, including glucose,
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xylose, mannose, galactose and arabinose. Therefore, it has more amorphous structure
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than cellulose [26]. It started to decompose at a lower temperature than cellulose and
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then slowly decomposed under a broad temperature range. The DTA curve of hemicellulose pyrolysis had two main peaks because of its different main structures, glucose and xylan.
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Lignin has a very complex structure and consists of three common phenylpropane structures: p-hydroxyphenyl, syringyl, and guaiacyl units. It is thus the most difficult
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one to decompose. Its decomposition happened slowly at a lower temperature and finished at the highest temperature. In a comparison of DTA curves of hemicellulose
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and lignin, DTA obtained from lignin pyrolysis had the lowest magnitude with the
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widest range. Moreover, the amount of remaining solid from lignin pyrolysis was higher than those for hemicellulose and cellulose due to its largely complex structure
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that mostly contains aromatics.
Effect of heating rate is also demonstrated in Fig. 1. It can be seen that the TGA curves and DTA peaks shifted towards higher temperature with an increase in the
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heating rate. This was because of the thermal lag at a higher heating rate at which time the difference of temperature between sample and furnace was greater than those at lower heating rates, resulting in slower decomposition [27]. These results are correspondent with some of the published literature [28, 29].
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Figure 1 TG/DTA curves of cellulose (a), hemicellulose (b) and lignin (c)
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at different heating rates.
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3.2 Pyrolysis of mixed components The pyrolysis of two-component mixtures and three-component mixtures were explored. The TGA and DTA curves of pyrolysis of mixtures at 5 oC·min-1 are shown in
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Fig. 2. Fig. 2(a) presents TGA and DTA curves of cellulose and hemicellulose mixtures. It can be seen that two main peaks of DTA curves appeared. The first peak might belong to the decomposition of the hemicellulose proportion since it appeared at the same position as can be observed in a case of pyrolysis of pure hemicellulose. The second peak belongs to the cellulose proportion for the same reason. Moreover, the quantity of the components are demonstrated by the means of peak magnitude. For example, the magnitude of the first peak decreased with the decrease of the hemicellulose fraction. Fig. 2(b) presents the TGA and DTA curves of cellulose and lignin mixtures. The DTA curve had only one main peak and its magnitude agreed with the mass fraction of cellulose. It was probably because all cellulose proportion decomposed within a short 9
ACCEPTED MANUSCRIPT temperature range as mentioned above. Therefore, the DTA curve of cellulose pyrolysis became dominant. The same phenomenon was found in a case of hemicellulose and lignin mixtures [Fig. 2(c)] which showed that the DTA curve had two main peaks at the
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same positions as the DTA curve obtained from pure hemicellulose pyrolysis.
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In the case of three-component mixtures, Fig. 2(d) shows TGA and DTA curves of
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the pyrolysis of mixtures. It can be seen that the DTA curves have two or three main peaks depending on the mass fraction of cellulose and hemicellulose. For the results of other heating rates, the TGA and DTA curves showed the same trend as Fig. 2, but the
(a)
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curves shifted forward with an increase in heat rates (the results not shown here).
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(c)
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Fig. 2 TG/DTA curves of synthesized biomass: cellulose: hemicellulose (a), cellulose: lignin (b), hemicellulose: lignin (c) and cellulose: hemicellulose: lignin (d)
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at different ratios.
3.3 The selection of analytical method
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Before all kinetic parameters were calculated, a suitable analytical method had to be selected from two different cases: for n=1 and n1. Fig. 3 illustrates the relationship
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between conversion and temperature of the three-component mixture from TGA and
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two analytical methods at 5oC·min-1. It shows that the curves from the analytical method for n≠1 was closed to the curve obtained from TGA (R2 = 0.96) when
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compared to the curves obtained from the analytical method for n=1. Therefore, this method (n≠1) is accurate enough and was thus chosen for the further analysis. From the TGA and DTA data, the activation energy, frequency factor and reaction
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order of each composition were calculated by the selected method as described in Section 2.3. Then, the effect of component proportion on each kinetic parameter and mathematical relation of biomass constituents and kinetic parameters were identified as described below.
Figure 3 The curve obtained from experimental data (cellulose: hemicellulose: lignin = 11
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3.4 Calculated kinetic parameters
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The TGA curves were fitted according to the analytical methods for n=1 [Eq. (4)] and n1 [Eq. (5)]. The kinetic parameters for both cases were then calculated as
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presented in Table 2. Among pure components (Sample nos. 6, 11 and 12), pyrolysis of cellulose showed the highest Ea and A as described in Section 3.1. However, the trend of kinetic parameters obtained from other synthesized biomass could not be clearly
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observed. This might be due to the interaction between biomass components.
Table 2. Calculated kinetic parameters of biomass pyrolysis from analytical methods (for reaction order = 1 and reaction order 1). n1
n=1
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Sample code
A /s-1
n
Ea /kJ·mol-1
A /s-1
18.18
9.78×105
9..4
47..9
6.41×105
881.47
6.43×108
...5
15.7.
6.03×104
.9.94
3.62×104
..47
879..5
1.70×106
4
.9.9.
4.00×104
5.99
871.11
2.81×106
5
89...1
1.10×109
..49
41.11
2.53×107
6
8.4.81
2.63×1013
8.11
891.81
3.67×1012
7
88...4
6.38×108
..71
871.99
1.55×107
8
14.79
2.07×106
5.91
871.48
2.13×106
9
19.51
6.80×107
..11
879.91
1.79×107
10
.8.44
1.14×105
5.9.
54.11
6.67×105
11
...95
3.62×104
5.79
871.99
4.40×105
12
5..19
1.13×105
..5.
14.45
1.79×106
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Ea /kJ·mol-1
2
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3
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1
12
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19.7.
13
5.11
4...9
1.79×106
Average data from eight replicates
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Moreover, the trend of kinetic parameters obtained from both analytical methods
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was different because the changes in reaction order influenced the changes of other
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parameters during the calculation. From the results obtained from Section 3.3, an analytical model for n1 showed a better fit to the experimental data. Therefore, the
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kinetic parameters calculated from this model were next used in the statistical analysis.
3.5 Statistical analysis and modeling
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To identify the effect of each biomass components on kinetic parameters and generate a statistical model for prediction of kinetic parameters, the RSM was
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employed. An analysis of variance (ANOVA) for Ea, A and n are shown in Table 3(a),
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3(b) and 3(c), respectively. The p-value shows the probability of kinetic parameters affected by the main composition during biomass pyrolysis. The terms which have
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p-value less than 0.05 have an important effect on the biomass pyrolysis. The ANOVA indicated that all independent biomass components: cellulose (X1), hemicellulose (X2) and lignin (X3), were the most significant factors affecting Ea and A. In case of n,
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independent components and two-level interaction of components, including X1X2, X1X3 and X2X3, were significant factors. To verify the ANOVA, the plots of normal probability of residuals, residuals versus predicted value and residuals versus effect for Ea, A and n were generated and are shown in Figs. A1, A2 and A3, respectively. The normal probability plots for all parameters show a straight line, indicating normal distribution of errors. The plots of residuals versus predicted value and residuals versus effect show that the residuals have no pattern. The validation revealed that there were no problems in the analysis. The final mathematical models associated to the responses in terms of actual factors [Eqs. (6)] are shown below.
Ea = 153.35X1 + 74.94X2 + 75.77X3
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(8)
n = 2.11X1 + 5.70X2 + 5.71X3 + 6.87X1X2 + 11.49X1X3 + 6.45X2X3
(9)
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From these regression models, the direction and magnitude of the constant
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coefficient of each term demonstrated that cellulose proportion had the most effect on
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Ea and A. An increase in the mass fraction of cellulose increased Ea and A, as a simple repeating unit of cellulose can be dramatically decomposed within a short time and temperature range. Therefore, an increase in this fraction leads to the simple
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decomposition of biomass at appropriate temperature, resulting in higher Ea and A. For n, interaction between the mass fractions of cellulose (X1) and lignin (X3) had the most
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effect.
The ternary plots were generated by the regression models. Fig. 4 shows the
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ternary contour plots indicating the effects of the interaction between main components
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and Ea [Fig. 4(a)], A [Fig. 4(b)] and n [Fig. 4(c)]. The effects of the components and interactions on the kinetic parameters can observed by these plots. These results
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provide useful information to predict the kinetic parameters of biomass pyrolysis at
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different compositions.
Table 3(a). The analysis of variance (ANOVA) of activation energy Source
Sum of Squares
DF
Mean Square
F Value
p-value > F
Model
7419.70
6
1236.62
7.78
0.01a
Linear Mixture
6401.55
2
3200.77
20.14
0.01a
X1X2
850.23
1
850.23
5.35
0.06
X1X3
42.46
1
42.46
0.27
0.62
X2X3
82.20
1
82.2
0.52
0.49
X1X2X3
43.26
1
43.26
0.27
0.62
Residual
953.45
6
158.91
Total
8373.15
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Table 3(b). The analysis of variance (ANOVA) of frequency factor
Sum of Squares
DF
Mean Square
F Value
p-value > F
Model
45.00
2
22.50
8.69
0.01a
Linear Mixture
45.00
2
22.50
8.69
0.01a
Residual
25.90
10
2.59
Total
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Source
Sum of Squares
DF
Mean Square
F Value
p-value > F
Model
22.01
6
3.67
15.16
0.01a
Linear Mixture
9.99
2
4.99
20.63
0.01a
X1X2
2.39
1
2.39
9.87
0.02a
X1X3
6.69
1
6.69
27.65
0.01a
X2X3
2.11
1
2.11
8.70
0.03a
0.83
1
0.83
3.43
0.11
1.45
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Residual Total
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Source
X1X2X3
23.46
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Significant F-values at the 95% confidence level (p-value 0.05). DF = Degrees of freedom
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Table 3(c). The analysis of variance (ANOVA) of reaction order
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Figure 4 Ternary contour plots of predicted activation energy (a), logarithm of frequency factor
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(b) and reaction order (c).
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3.6 Application to real biomass The biomass sample, Leucaena Leucocephala, with known composition was used
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to test the accuracy of models. Its composition was substituted into Eqs. (7) – (9) in order to calculate the kinetic parameters. Then, the calculated parameters were substituted into Eq. (5). The relationship between temperature and conversion was plotted and compared to the TGA curve as shown in Fig. 5. The R2 value was close to unity (0.98) which demonstrates a good agreement of the generated model and experimental data. In addition, these models were applied to calculate the kinetic values of other biomass pyrolysis. The results and R2 values are presented in Table 4. As high R2 values were obtained, the analytical method for n1 and statistical analysis have the potential to identify the correlation between biomass components and kinetic values for biomass pyrolysis.
Table 4. Predicted kinetic values of different biomass 16
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Cellulose
Hemicellulose
Lignin
R2
Ref.
Pine cone
34.35
39.50
26.16
0.87
[30]
Rice straw
35.56
39.67
24.78
0.90
[31]
Parinari fruit shell
55.43
7.81
36.75
0.83
[32]
Poplar wood
44.79
29.17
26.04
0.94
Mongolia oak tree
36.69
30.51
32.80
0.89
[34]
Launa wood
40.49
15.74
Wheat straw
41.00
35.00
Corn cob
41.58
50.64
Sugarcane
53.17
27.78
Corn straw
56.73
31.58
[33]
43.77
0.92
[35]
24.00
0.90
[36]
7.78
0.93
[37]
19.05
0.96
[37]
11.69
0.98
[37]
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Biomass
However, while the error was acceptable with respect to R2 value, the difference
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between experimental and predicted data was detected at moderate temperature (Fig.
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5). This is due to the assumption of the kinetic model. In addition, xylan and
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organosolv lignin were used for the hemicellulose and lignin model, respectively, since these forms of biomass components are largely found in nature. Still, while their behaviors are different from real hemicellulose and lignin, the obtained model can be
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used as a simple approximation for predicting the overall trend of chemical reaction kinetics.
To generate a more precise conversion curve, other solid-state kinetic reactions,
including nucleation, geometrical contraction and diffusion, should be applied into the kinetic models. In real biomass, the interaction between all components, particles-volatiles and the effect of alkali and alkaline earth metal species can be very strong [15, 38]. However, the effect of ash on kinetic parameters is not important comparing to the used reactive species. Thus, besides applying other solid-state kinetic reactions, the terms for those involved the interaction should be included for more accuracy.
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Figure 5 The conversion curves obtained from experimental data and predicted data of pyrolysis of Leucaena Leucocephala at 20 oC·min-1; R2 = 0.98.
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4 CONCLUSIONS
The relationship between biomass constituent and kinetic parameters for biomass
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pyrolysis was determined. Response surface methodology and simplex-lattice mixture design were used for this purpose. The results show that a model obtained from the
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analytical method (for n1) can be appropriately used to calculate the activation energy,
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frequency factor reaction order from thermogravimetric analysis data that provided a high regression coefficient. From statistical analysis, cellulose has the most significant
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effect on the activation energy and the frequency factor while interaction between cellulose and lignin have the most effect on the reaction order. The generated regression models for each kinetic parameter were used to produce ternary contour
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plots and determine the kinetic parameters. Then, Leucaena Leucocephala was used as an example for the accuracy test of these models. The results show that the calculated kinetic values provide a very acceptable thermogravimetric analysis curve compared to the curve obtained from the experiment. It is expected that this work provides useful information for estimating the kinetic parameters for other biomass pyrolysis and designing suitable process operating conditions.
ACKNOWLEDGEMENTS The authors would like to express their thanks to the Graduate School, Chulalongkorn University for partial financial support. The authors also thank the
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editing.
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(a)
Figure A1 Normal probability plot of residuals (a), residual vs predicted values (b) and residual vs factor (c) for the prediction of activation energy.
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Figure A2 Normal probability plot of residuals (a), residual vs predicted values (b) and residual vs factor (c) for the prediction of logarithm of frequency factor.
(a)
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Fig. A3. Normal probability plot of residuals (a), residual vs. predicted values (b) and residual vs
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factor (c) for the prediction of reaction order.
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Graphical Abstract
(a)
(b)
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(c)
Ternary contour plots of predicted activation energy (a), logarithm of frequency factor (b) and
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reaction order (c)
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