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Journal of Thermal Analysis and Calorimetry

, Volume 124, Issue 3, pp 1641–1649 | Cite as

Combustion kinetics of pine sawdust biochar

Data smoothing and isoconversional kinetic analysis
  • Yang Yu
  • Xiaoxu Fu
  • Lili Yu
  • Ronghou Liu
  • Junmeng Cai
Article

Abstract

The combustion kinetics of biochar derived from fast pyrolysis of pine sawdust was investigated by using nonisothermal thermogravimetric analysis under air atmosphere at different heating rates. The experimental conversion (αT) curves contain some experimental errors, and the corresponding derivative conversion (dα/dTT) curves have many fluctuations. Two strategies of obtaining the smooth dα/dTT curves were used based on the robust locally weighted scatterplot smoothing method coupled with the corrected AIC smoothing parameter selection method. In the strategy of firstly smoothing αT data and then differentiating smoothed αT data, although the smoothed αT data are relatively smooth, the dα/dTT curves obtained from the differentiation of the smoothed αT data have fluctuations. In the strategy of firstly differentiating αT data and then smoothing dα/dTT data, the obtained dα/dTT curves with the optimal smoothing parameters are smooth enough, which indicates that the second strategy is effective. The isoconversional kinetic analysis of the smoothed dα/dTT curves was performed by using the Friedman differential isoconversional method. The effective activation energies of biochar combustion were obtained and found to significantly vary with the conversion degree (from about 150 to 250 kJ mol−1 in the range of conversion degree from 0.05 to 0.95).

Keywords

Biochar Combustion Kinetics Data smoothing Biomass Isoconversional methods 

Notes

Acknowledgements

Financial support from the National Natural Science Foundation of China (Grant No. 51176121) and the National High Technology Research and Development Program of China (863 Program, Grant No. 2012AA101808-05) is greatly acknowledged.

References

  1. 1.
    Gabbott P. Principles and applications of thermal analysis. New York: Wiley; 2008.CrossRefGoogle Scholar
  2. 2.
    White JE, Catallo WJ, Legendre BL. Biomass pyrolysis kinetics: a comparative critical review with relevant agricultural residue case studies. J Anal Appl Pyrolysis. 2011;91(1):1–33.CrossRefGoogle Scholar
  3. 3.
    Sebio-Puñal T, Naya S, López-Beceiro J, Tarrío-Saavedra J, Artiaga R. Thermogravimetric analysis of wood, holocellulose, and lignin from five wood species. J Therm Anal Calorim. 2012;109(3):1163–7.CrossRefGoogle Scholar
  4. 4.
    Yi Q, Qi F, Cheng G, Zhang Y, Xiao B, Hu Z, et al. Thermogravimetric analysis of co-combustion of biomass and biochar. J Therm Anal Calorim. 2013;112(3):1475–9.CrossRefGoogle Scholar
  5. 5.
    Nappi L, Liu C. Advanced kinetic study on combustion of tobacco char by TG-FTIR. In: Skevis G, Sardi K, editors. The third European combustion meeting. Crete: Mediterranean Agronomic Institute of Chania; 2007. p. 1–6.Google Scholar
  6. 6.
    Adnadevic B, Mojovic Z, Abu Rabi A, Jovanovic J. Isoconversional kinetic analysis of isothermal selective ethanol adsorption on zeolite type NaZSM-5. Chem Eng Technol. 2007;30(9):1228–34.CrossRefGoogle Scholar
  7. 7.
    Puiu M, Constantinovici M, Babaligea I, Raducan A, Olmazu C, Oancea D. Detecting operational inactivation of horseradish peroxidase using an isoconversional method. Chem Eng Technol. 2010;33(3):414–20.CrossRefGoogle Scholar
  8. 8.
    Sbirrazzuoli N, Vincent L, Mija A, Guigo N. Integral, differential and advanced isoconversional methods: complex mechanisms and isothermal predicted conversion–time curves. Chemom Intell Lab Syst. 2009;96(2):219–26.CrossRefGoogle Scholar
  9. 9.
    Caballero JA, Conesa JA. Mathematical considerations for nonisothermal kinetics in thermal decomposition. J Anal Appl Pyrolysis. 2005;73:85–1000.CrossRefGoogle Scholar
  10. 10.
    Wu W, Mei Y, Zhang L, Liu R, Cai J. Kinetics and reaction chemistry of pyrolysis and combustion of tobacco waste. Fuel. 2015;156:71–80.CrossRefGoogle Scholar
  11. 11.
    Mamleev V, Bourbigot S, Le Bras M, Yvon J, Lefebvre J. Model-free method for evaluation of activation energies in modulated thermogravimetry and analysis of cellulose decomposition. Chem Eng Sci. 2006;61(4):1276–92.CrossRefGoogle Scholar
  12. 12.
    Simonoff JS. Smoothing methods in statistics. New York: Springer; 2012.Google Scholar
  13. 13.
    Cai J, Liu R, Sun C. Logistic regression model for isoconversional kinetic analysis of cellulose pyrolysis. Energy Fuels. 2008;22(2):867–70.CrossRefGoogle Scholar
  14. 14.
    Naya S, Cao R, Artiaga R. Local polynomial estimation of TG derivatives using logistic regression for pilot bandwidth selection. Thermochim Acta. 2003;406(1):177–83.CrossRefGoogle Scholar
  15. 15.
    Cai J, Chen S, Liu R. Weibull mixture model for isoconversional kinetic analysis of biomass oxidative pyrolysis. J Energy Inst. 2009;82(4):238–41.CrossRefGoogle Scholar
  16. 16.
    Janković B. The kinetic modeling of the non-isothermal pyrolysis of Brazilian oil shale: application of the Weibull probability mixture model. J Pet Sci Eng. 2013;111:25–36.CrossRefGoogle Scholar
  17. 17.
    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.CrossRefGoogle Scholar
  18. 18.
    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.CrossRefGoogle Scholar
  19. 19.
    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;119:1429–38.CrossRefGoogle Scholar
  20. 20.
    Lee JS, Cox DD. Robust smoothing: smoothing parameter selection and applications to fluorescence spectroscopy. Comput Stat Data Anal. 2010;54(12):3131–43.CrossRefGoogle Scholar
  21. 21.
    Keele LJ. Semiparametric regression for the social sciences. New York: Wiley; 2008.Google Scholar
  22. 22.
    Craig SG, Ng PT. Using quantile smoothing splines to identify employment subcenters in a multicentric urban area. J Urban Econ. 2001;49(1):100–20.CrossRefGoogle Scholar
  23. 23.
    Okazaki T. Normalization of DNA microarray data with BIC model comparison. IJCSNS. 2014;14(3):10.Google Scholar
  24. 24.
    Várhegyi G, Till F. Computer processing of thermogravimetric-mass spectrometric and high pressure thermogravimetric data. Part 1. Smoothing and differentiation. Thermochim Acta. 1999;329(2):141–5.CrossRefGoogle Scholar
  25. 25.
    Chen HX, Liu NA, Shu LF, Zong RW. Smoothing and differentiation of thermogravimetric data of biomass materials. J Therm Anal Calorim. 2004;78(3):1029–41.CrossRefGoogle Scholar
  26. 26.
    Liu N, Chen H, Shu L, Zong R, Yao B, Statheropoulos M. Gaussian smoothing strategy of thermogravimetric data of biomass materials in an air atmosphere. Ind Eng Chem Res. 2004;43(15):4087–96.CrossRefGoogle Scholar
  27. 27.
    Wang X, Hu Z, Deng S, Wang Y, Tan H. Kinetics investigation on the combustion of biochar in O2/CO2 atmosphere. Environ Prog Sustain Energy. 2014;34(3):923–32.CrossRefGoogle Scholar
  28. 28.
    Cleveland WS. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc. 1979;74(368):829–36.CrossRefGoogle Scholar
  29. 29.
    Cleveland WS, Devlin SJ. Locally weighted regression: an approach to regression analysis by local fitting. J Am Stat Assoc. 1988;83(403):596–610.CrossRefGoogle Scholar
  30. 30.
    Jacoby WG. Loess: a nonparametric, graphical tool for depicting relationships between variables. Electoral Stud. 2000;19(4):577–613.CrossRefGoogle Scholar
  31. 31.
    Manchester L. Empirical influence for robust smoothing. Aus J Stat. 1996;38(3):275–90.CrossRefGoogle Scholar
  32. 32.
    Guseva O, Lichtblau A. Application of smoothing methods for estimation of service life for polymers from tensile testing. Polym Test. 2005;24(6):718–27.CrossRefGoogle Scholar
  33. 33.
    Isnanto R. Comparation on several smoothing methods in nonparametric regression. J Sist Komput. 2011;1(1):41–7.Google Scholar
  34. 34.
    Cohen RA, editors. An introduction to PROC LOESS for local regression. In: Proceedings of the 24th SAS users group international conference, paper 273; 1999.Google Scholar
  35. 35.
    Hurvich CM, Simonoff JS, Tsai CL. Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. J R Stat Soc B. 1998;60(2):271–93.CrossRefGoogle Scholar
  36. 36.
    Vyazovkin S. Isoconversional kinetics of thermally stimulated processes. New York: Springer; 2015.CrossRefGoogle Scholar
  37. 37.
    Wu W, Mei Y, Zhang L, Liu R, Cai J. Effective activation energies of lignocellulosic biomass pyrolysis. Energy Fuels. 2014;28(6):3916–23.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2016

Authors and Affiliations

  • Yang Yu
    • 1
  • Xiaoxu Fu
    • 1
  • Lili Yu
    • 1
  • Ronghou Liu
    • 1
  • Junmeng Cai
    • 1
  1. 1.Biomass Energy Engineering Research Center, Key Laboratory of Urban Agriculture (South) Ministry of Agriculture, School of Agriculture and BiologyShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China

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