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Kinetic analysis of biomass pyrolysis using a double distributed activation energy model

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Abstract

Pyrolysis is a fundamental step in thermochemical processes of biomass materials, so a suitable kinetic model is an essential tool to predict the evolution of the resulting products of reaction. However, many difficulties arise in modeling this process step due to the very high number of the involved reactions. In this work, a new double-Gaussian distributed activation energy model was applied in fitting the experimental data of olive residue pyrolysis obtained by thermogravimetric analysis. 2-DAEM formulation considers two sets of parallel reactions occurring and sharing the same pre-exponential factor, but shows different distributions of the activation energy, described by two separate Gaussian distributions that, in turn, grasp the two pyrolysis steps with a high accuracy. Since it is well known that in fitting all the kinetic parameters the pre-exponential factor results highly correlated with the activation energy, the former parameter was separately estimated as a linear combination of the values obtained for the three main biomass components, cellulose, hemicellulose and lignin.

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Abbreviations

DAEM:

Distributed activation energy model

EF:

Extractive free

TG:

Thermogravimetry

DTG:

Derivative thermogravimetry

E :

Activation energy (kJ mol−1)

E 0 :

Mean activation energy (kJ mol−1)

f(E):

Distribution function of activation energy (mol J−1)

k 0 :

Frequency factor (s−1)

k :

Kinetic constant (mol s−1)

T :

Absolute temperature (K)

R :

Universal gas constant (8.314 J mol−1 K−1)

t :

Time of conversion (s)

m 0 :

The initial mass (mass%)

m f :

The final residual mass (mass%)

m t :

The mass of the sample at time t (mass%)

N :

Number of data points

x :

Mass fraction of released volatiles

X :

SSR, sum of square residuals

y s :

Experimental data in fitting function

y(T):

Calculated data in fitting function

V :

Accumulated volatiles produced

V * :

Final accumulated volatiles produced

w :

Mass of primary/secondary pyrolysis

α :

Heating rate (K min−1)

σ E :

Standard deviation

1:

First Gaussian in 2-DAEM

2:

Second Gaussian in 2-DAEM

i :

ith component

j :

jth experimental data to fitting

References

  1. Mohan D, Pittman CU, Steele PH. Pyrolysis of wood/biomass for bio-oil: a critical review. Energy Fuels. 2006;20(3):848–89. doi:10.1021/ef0502397.

    Article  CAS  Google Scholar 

  2. Kader MA, Islam MR, Parveen M, Haniu H, Takai K. Pyrolysis decomposition of tamarind seed for alternative fuel. Bioresour Technol. 2013;149:1–7. doi:10.1016/j.biortech.2013.09.032.

    Article  CAS  Google Scholar 

  3. Ion I, Popescu F, Rolea G. A biomass pyrolysis model for CFD application. J Therm Anal Calorim. 2013;111(3):1811–5. doi:10.1007/s10973-012-2552-7.

    Article  CAS  Google Scholar 

  4. 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. doi:10.1016/j.biortech.2007.06.033.

    Article  CAS  Google Scholar 

  5. Bates RB, Ghoniem AF. Biomass torrefaction: modeling of volatile and solid product evolution kinetics. Bioresour Technol. 2012;124:460–9. doi:10.1016/j.biortech.2012.07.018.

    Article  CAS  Google Scholar 

  6. Pitt G. The kinetics of the evolution of volatile products from coal. Fuel. 1962;41(3):267–74.

    CAS  Google Scholar 

  7. Ma F, Zeng Y, Wang J, Yang Y, Yang X, Zhang X. Thermogravimetric study and kinetic analysis of fungal pretreated corn stover using the distributed activation energy model. Bioresour Technol. 2013;128:417–22. doi:10.1016/j.biortech.2012.10.144.

    Article  CAS  Google Scholar 

  8. Li Z, Liu C, Chen Z, Qian J, Zhao W, Zhu Q. Analysis of coals and biomass pyrolysis using the distributed activation energy model. Bioresour Technol. 2009;100(2):948–52. doi:10.1016/j.biortech.2008.07.032.

    Article  CAS  Google Scholar 

  9. Trninić M, Wang L, Várhegyi G, Grønli M, Skreiberg Ø. Kinetics of corncob pyrolysis. Energy Fuels. 2012;26(4):2005–13. doi:10.1021/ef3002668.

    Article  Google Scholar 

  10. Li C, Suzuki K. Kinetic analyses of biomass tar pyrolysis using the distributed activation energy model by TG/DTA technique. J Therm Anal Calorim. 2009;98(1):261–6. doi:10.1007/s10973-009-0151-z.

    Article  CAS  Google Scholar 

  11. Li L, Wang G, Wang S, Qin S. Thermogravimetric and kinetic analysis of energy crop Jerusalem artichoke using the distributed activation energy model. J Therm Anal Calorim. 2013;114(3):1183–9. doi:10.1007/s10973-013-3115-2.

    Article  CAS  Google Scholar 

  12. Cheng Z, Wu W, Ji P, Zhou X, Liu R, Cai J. Applicability of Fraser–Suzuki function in kinetic analysis of DAEM processes and lignocellulosic biomass pyrolysis processes. J Therm Anal Calorim. 2015;119(2):1429–38.

    Article  CAS  Google Scholar 

  13. Zhu X, Chen Z, Xiao B, Hu Z, Hu M, Liu C, et al. Co-pyrolysis behaviors and kinetics of sewage sludge and pine sawdust blends under non-isothermal conditions. J Therm Anal Calorim. 2014;. doi:10.1007/s10973-014-4321-2.

    Google Scholar 

  14. Albis A, Ortiz E, Suárez A, Piñeres I. TG/MS study of the thermal devolatilization of Copoazú peels (Theobroma grandiflorum). J Therm Anal Calorim. 2014;115(1):275–83. doi:10.1007/s10973-013-3227-8.

    Article  CAS  Google Scholar 

  15. Wu W, Mei Y, Zhang L, Liu R, Cai J. Effective activation energies of lignocellulosic biomass pyrolysis. Energy Fuels. 2014;28(6):3916–23.

    Article  CAS  Google Scholar 

  16. Soria-Verdugo A, Garcia-Gutierrez LM, Blanco-Cano L, Garcia-Hernando N, Ruiz-Rivas U. Evaluating the accuracy of the distributed activation energy model for biomass devolatilization curves obtained at high heating rates. Energy Convers Manag. 2014;86:1045–9.

    Article  Google Scholar 

  17. Anthony DB, Howard JB. Coal devolatilization and hydrogastification. AIChE J. 1976;22(4):625–56. doi:10.1002/aic.690220403.

    Article  CAS  Google Scholar 

  18. Cai JM, Liu RH. Parametric study of the nonisothermal n th-order distributed activation energy model involved the Weibull distribution for biomass pyrolysis. J Therm Anal Calorim. 2007;89(3):971–5. doi:10.1007/s10973-006-8266-y.

    Article  CAS  Google Scholar 

  19. Boudreau BP, Ruddick BR. On a reactive continuum representation of organic matter diagenesis. Am J Sci. 1991;291(5):507–38.

    Article  CAS  Google Scholar 

  20. Burnham AK, Braun RL. Global kinetic analysis of complex materials. Energy Fuels. 1998;13(1):1–22. doi:10.1021/ef9800765.

    Article  Google Scholar 

  21. Lakshmanan CC, Bennett ML, White N. Implications of multiplicity in kinetic parameters to petroleum exploration. Distributed activation energy models. Energy Fuels. 1991;5(1):110–7.

    Article  CAS  Google Scholar 

  22. Holstein A, Bassilakis R, Wójtowicz MA, Serio MA. Kinetics of methane and tar evolution during coal pyrolysis. Proc Combust Inst. 2005;30(2):2177–85. doi:10.1016/j.proci.2004.08.231.

    Article  Google Scholar 

  23. Sluiter A, Ruiz R, Scarlata C, Sluiter J, Templeton D, Crocker D. Determination of extractives in biomass. Laboratory Analytical Procedure (LAP), NREL/TP-510-42619. National Renewable Laboratory, Golden, CO. 2005.

  24. Cai J, Wu W, Liu R. Sensitivity analysis of three-parallel-DAEM-reaction model for describing rice straw pyrolysis. Bioresour Technol. 2013;132:423–6. doi:10.1016/j.biortech.2012.12.073.

    Article  CAS  Google Scholar 

  25. Sarvaramini A, Assima GP, Larachi F. Dry torrefaction of biomass: torrefied products and torrefaction kinetics using the distributed activation energy model. Chem Eng J. 2013;229:498–507.

    Article  CAS  Google Scholar 

  26. Liu Q, Zhong Z, Wang S, Luo Z. Interactions of biomass components during pyrolysis: a TG-FTIR study. J Anal Appl Pyrolysis. 2011;90(2):213–8. doi:10.1016/j.jaap.2010.12.009.

    Article  CAS  Google Scholar 

  27. De Caprariis B, De Filippis P, Herce C, Verdone N. Double-gaussian distributed activation energy model for coal devolatilization. Energy Fuels. 2012;26(10):6153–9.

    Article  Google Scholar 

  28. Várhegyi G, Chen H, Godoy S. Thermal decomposition of wheat, oat, barley, and Brassica carinata straws: a kinetic study. Energy Fuels. 2009;23(2):646–52. doi:10.1021/ef800868k.

    Article  Google Scholar 

  29. Zhang J, Chen T, Wu J, Wu J. Multi-Gaussian-DAEM-reaction model for thermal decompositions of cellulose, hemicellulose and lignin: comparison of N2 and CO2 atmosphere. Bioresour Technol. 2014;166:87–95. doi:10.1016/j.biortech.2014.05.030.

    Article  CAS  Google Scholar 

  30. Várhegyi G, Bobály B, 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. doi:10.1021/ef101079r.

    Article  Google Scholar 

  31. Brun R, Rademakers F. ROOT: an object oriented data analysis framework. Nucl Instrum Methods Phys Res Sect A. 1997;389(1–2):81–6.

    Article  CAS  Google Scholar 

  32. Di Blasi C, Signorelli G, Di Russo C, Rea G. Product distribution from pyrolysis of wood and agricultural residues. Ind Eng Chem Res. 1999;38(6):2216–24. doi:10.1021/ie980711u.

    Article  Google Scholar 

  33. Uzun BB, Pütün AE, Pütün E. Composition of products obtained via fast pyrolysis of olive-oil residue: effect of pyrolysis temperature. J Anal Appl Pyrolysis. 2007;79(1–2):147–53. doi:10.1016/j.jaap.2006.12.005.

    Article  CAS  Google Scholar 

  34. Brown ME, Galwey AK. The significance of “compensation effects” appearing in data published in “computational aspects of kinetic analysis”: ICTAC project, 2000. Thermochim Acta. 2002;387(2):173–83.

    Article  CAS  Google Scholar 

  35. Strezov V, Lucas JA, Evans TJ, Strezov L. Effect of heating rate on the thermal properties and devolatilisation of coal. J Therm Anal Calorim. 2004;78(2):385–97.

    Article  CAS  Google Scholar 

  36. Vyazovkin S, Burnham AK, Criado JM, Pérez-Maqueda LA, Popescu C, Sbirrazzuoli N. ICTAC Kinetics Committee recommendations for performing kinetic computations on thermal analysis data. Thermochim Acta. 2011;520(1–2):1–19.

    Article  CAS  Google Scholar 

  37. Yang H, Yan R, Chen H, Lee DH, Zheng C. Characteristics of hemicellulose, cellulose and lignin pyrolysis. Fuel. 2007;86(12–13):1781–8. doi:10.1016/j.fuel.2006.12.013.

    Article  CAS  Google Scholar 

  38. Granada E, Eguía P, Comesaña JA, Patiño D, Porteiro J, Miguez JL. Devolatilization behaviour and pyrolysis kinetic modelling of Spanish biomass fuels. J Therm Anal Calorim. 2013;113(2):569–78. doi:10.1007/s10973-012-2747-y.

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Dipartimento di Ingegneria Chimica Materiali Ambiente, Sapienza University of Rome, Via Eudossiana 18, 00184, Rome, Italy

    Benedetta de Caprariis, Maria Laura Santarelli, Marco Scarsella, Nicola Verdone & Paolo De Filippis

  2. CIRCE, Research Center on Energy Resources and Consumptions, Mariano Esquillor, 15, 50018, Saragossa, Spain

    Carlos Herce

Authors
  1. Benedetta de Caprariis
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  2. Maria Laura Santarelli
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  4. Carlos Herce
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  5. Nicola Verdone
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  6. Paolo De Filippis
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Correspondence to Carlos Herce.

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de Caprariis, B., Santarelli, M.L., Scarsella, M. et al. Kinetic analysis of biomass pyrolysis using a double distributed activation energy model. J Therm Anal Calorim 121, 1403–1410 (2015). https://doi.org/10.1007/s10973-015-4665-2

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  • DOI: https://doi.org/10.1007/s10973-015-4665-2

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