Abstract
In this work, a new method for fitting the conversion rate curves of the distributed activation energy model (DAEM) and lignocellulosic biomass pyrolysis process was introduced. The method was based on the curve fitting technique using the Fraser–Suzuki function. Various simulated DAEM processes were analyzed. The results showed that the conversion rate curve of one DAEM process could be described well by a Fraser–Suzuki function. According to the obtained parameters of the fitted Fraser–Suzuki functions, the influences of the DAEM parameters on the conversion rate curves of the corresponding DAEM processes can be quantitatively obtained. The experimental data of the pyrolysis of cotton stalk, oilseed rape straw, and rice straw were fitted by the Fraser–Suzuki mixture model which involves three individual Fraser–Suzuki functions. It has been found that the Fraser–Suzuki mixture model can reproduce accurately the conversion rate curves of the pyrolysis of three lignocellulosic biomass samples. The Fraser–Suzuki mixture model provides an approach to separate lignocellulosic biomass pyrolysis into three parallel reactions which link to the decomposition of hemicellulose, cellulose, and lignin, respectively.
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References
White JE, Catallo WJ, Legendre BL. Biomass pyrolysis kinetics: a comparative critical review with relevant agricultural residue case studies. J Anal Appl Pyrolsis. 2011;91(1):1–33.
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(1):232–42.
Barbadillo F, Fuentes A, Naya S, Cao R, Mier JL, Artiaga R. Evaluating the logistic mixture model on real and simulated TG curves. J Therm Anal Calorim. 2007;87(1):223–7.
Cao R, Naya S, Artiaga R, García A, Varela A. Logistic approach to polymer degradation in dynamic TGA. Polym Degrad Stab. 2004;85(1):667–74.
Naya S, Cao R, de Ullibarri IL, Artiaga R, Barbadillo F, García A. Logistic mixture model versus Arrhenius for kinetic study of material degradation by dynamic thermogravimetric analysis. J Chemom. 2006;20(3–4):158–63.
Cai J, Alimujiang S. Kinetic analysis of wheat straw oxidative pyrolysis using thermogravimetric analysis: statistical description and isoconversional kinetic analysis. Ind Eng Chem Res. 2009;48(2):619–24.
Cai J, Chen SY, Liu RH. Weibull mixture model for isoconversional kinetic analysis of biomass oxidative pyrolysis. J Energy Inst. 2009;82(4):238–41.
Cai J, Liu R. Weibull mixture model for modeling nonisothermal kinetics of thermally stimulated solid-state reactions: application to simulated and real kinetic conversion data. J Phys Chem B. 2007;111(36):10681–6.
Cai J, Liu R. Application of Weibull 2-mixture model to describe biomass pyrolysis kinetics. Energy Fuels. 2008;22(1):675–8.
Kuo-Chao L, Keng-Tung W, Chien-Song C, Wei-The T. A new study on combustion behavior of pine sawdust characterized by the Weibull distribution. Chin J Chem Eng. 2009;17(5):860–8.
Yoshikawa M, Yamada S, Koga N. Phenomenological interpretation of the multistep thermal decomposition of silver carbonate to form silver metal. J Phys Chem C. 2014;118(15):8059–70.
Perejón A, Sánchez-Jiménez PE, Criado JM, Pérez-Maqueda LA. Kinetic analysis of complex solid-state reactions. A new deconvolution procedure. J Phys Chem B. 2011;115(8):1780–91.
Findoráková L, Svoboda R. Kinetic analysis of the thermal decomposition of Zn (II) 2-chlorobenzoate complex with caffeine. Thermochim Acta. 2012;543:113–7.
Svoboda R, Málek J. Applicability of Fraser-Suzuki function in kinetic analysis of complex crystallization processes. J Therm Anal Calorim. 2013;111(2):1045–56.
Koga N, Goshi Y, Yamada S, Pérez-Maqueda L. Kinetic approach to partially overlapped thermal decomposition processes. J Therm Anal Calorim. 2013;111(2):1463–74.
Koga N, Goshi Y, Yamada S, Pérez-Maqueda LA. Kinetic approach to partially overlapped thermal decomposition processes. J Therm Anal Calorim. 2013;111(2):1463–74.
Wu W, Cai J, Liu R. Isoconversional kinetic analysis of distributed activation energy model processes for pyrolysis of solid fuels. Ind Eng Chem Res. 2013;52(40):14376–83.
Gb Várhegyi, Bz Bobály. Jakab E, Chen H. Thermogravimetric study of biomass pyrolysis kinetics. A distributed activation energy model with prediction tests. Energy Fuels. 2010;25(1):24–32.
Sonobe T, Worasuwannarak N. Kinetic analyses of biomass pyrolysis using the distributed activation energy model. Fuel. 2008;87(3):414–21.
NREL. Determination of structural carbohydrates and lignin in biomass. Colorado, USA: National Renewable Energy Laboratory; 2011.
ASTM. Standard practice for preparation of biomass for compositional analysis. Pennsylvania, USA: ASTM International; 2007.
Fraser R, Suzuki E. Resolution of overlapping absorption bands by least squares procedures. Anal Chem. 1966;38(12):1770–3.
Fraser RD, Suzuki E. Resolution of overlapping bands. Functions for simulating band shapes. Anal Chem. 1969;41(1):37–9.
Felinger A. Data analysis and signal processing in chromatography. Amsterdam: Elsevier Science; 1998.
Scheeren P, Barna P, Smit H. A software package for the evaluation of peak parameters in an analytical signal based on a non-linear regression method. Anal Chim Acta. 1985;167:65–80.
Brenner JR. Data analysis made easy with datafit. Chem Eng Educ. 2006;40(1):60–5.
Cai J, Liu R. New distributed activation energy model: numerical solution and application to pyrolysis kinetics of some types of biomass. Bioresour Technol. 2008;99(8):2795–9.
Burnham AK, Braun RL. Global kinetic analysis of complex materials. Energy Fuels. 1999;13(1):1–22.
Cai J, Wu W, Liu R. An overview of distributed activation energy model and its application in the pyrolysis of lignocellulosic biomass. Renew Sustain Energy Rev. 2014;36:236–46.
Glynn J, Gray TW. The Beginner’s Guide to Mathematica, Version 4. Cambridge: Cambridge University Press; 2000.
Güneş M, Güneş S. The influences of various parameters on the numerical solution of nonisothermal DAEM equation. Thermochim Acta. 1999;336(1–2):93–6.
Cai J, He F, Yao F. Nonisothermal nth-order DAEM equation and its parametric study—use in the kinetic analysis of biomass pyrolysis. J Math Chem. 2007;42(4):949–56.
Wu W, Mei Y, Zhang L, Liu R, Cai J. Effective activation energies of lignocellulosic biomass pyrolysis. Energy Fuels. 2014;28(6):3916–23.
Orfao JJM, Antunes FJA, Figueiredo JL. Pyrolysis kinetics of lignocellulosic materials—three independent reactions model. Fuel. 1999;78(3):349–58.
Manyà JJ, Velo E, Puigjaner L. Kinetics of biomass pyrolysis: a reformulated three-parallel-reactions model. Ind Eng Chem Res. 2003;42(3):434–41.
Acknowledgements
Financial support was obtained from School of Agriculture and Biology, Shanghai Jiao Tong University (Grant No. NRC201101). The authors would like to acknowledge Professor Lius A. Pérez-Maqueda for his help in the parameter estimation of the Fraser–Suzuki function and Miss Lu Wang for providing TG data.
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Cheng, Z., Wu, W., Ji, P. et al. Applicability of Fraser–Suzuki function in kinetic analysis of DAEM processes and lignocellulosic biomass pyrolysis processes. J Therm Anal Calorim 119, 1429–1438 (2015). https://doi.org/10.1007/s10973-014-4215-3
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DOI: https://doi.org/10.1007/s10973-014-4215-3