Torrefaction is a type of thermo-chemical pretreatment process to enhance energy density of lignocellulosic fuels. For a torrefaction process, a key challenge is to develop efficient thermal conversion technologies for torrefied fuels which can compete with fossil fuels. The calculation of chemical exergy is an essential step for designing efficient thermal conversion systems. However, there is a few correlations to predict the chemical exergy of solid fuels has been published so far.  This study deals with a new method to characterize the chemical exergy of different kinds of torrefied lignocellulosic fuels by using Bayesian trained artificial neural network (ANN). The proposed model based on proximate analysis and higher heating values of torrefied fuels. Use of the artificial neural network method is encouraged to reduce variance in model results. The results indicate that the proposed model offers a high degree of correlation (R²=0,9999) and its robustness and capability to compute the chemical exergy of any torrefied lignocellulosic fuels from its proximate analysis and heating value.

• 1. Rousset P, Aguiar C, Labbe N, Commandre JM. Enhancing the combustible properties of bamboo by torrefaction. Bioresource Technology. 2011 Sep; 102(17): 8225-8231.
• 2. Song GH, Shen LH, Xiao J. Estimating Specific Chemical Exergy of Biomass from Basic Analysis Data. Industrial & Engineering Chemistry Research. 2011 Aug 17; 50(16): 9758-9766.
• 3. Parikh J, Channiwala SA, Ghosal GK. A correlation for calculating HHV from proximate analysis of solid fuels. Fuel. 2005 Mar; 84(5): 487-494.
• 4. Estiati I, Freire FB, Freire JT, Aguado R, Olazar M. Fitting performance of artificial neural networks and empirical correlations to estimate higher heating values of biomass. Fuel. 2016 Sep 15; 180: 377-383.
• 5. Nhuchhen DR. Prediction of carbon, hydrogen, and oxygen compositions of raw and torrefied biomass using proximate analysis. Fuel. 2016 Sep 15; 180: 348-356.
• 6. Arias B, Pevida C, Fermoso J, Plaza MG, Rubiera F, Pis JJ. Influence of torrefaction on the grindability and reactivity of woody biomass. Fuel Processing Technology. 2008 Feb; 89(2): 169-175.
• 7. Bridgeman TG, Jones JM, Williams A, Waldron DJ. An investigation of the grindability of two torrefied energy crops. Fuel. 2010 Dec; 89(12): 3911-3918.
• 8. Chen DY, Zhou JB, Zhang QS, Zhu XF, Lu Q. Upgrading of Rice Husk by Torrefaction and its Influence on the Fuel Properties. Bioresources. 2014 Nov; 9(4): 5893-5905.
• 9. Eseltine D, Thanapal SS, Annamalai K, Ranjan D. Torrefaction of woody biomass (Juniper and Mesquite) using inert and non-inert gases. Fuel. 2013 Nov; 113: 379-388.
• 10. Ibrahim RHH, Darvell LI, Jones JM, Williams A. Physicochemical characterisation of torrefied biomass. Journal of Analytical and Applied Pyrolysis. 2013 Sep; 103: 21-30.
• 11. Pala M, Kantarli IC, Buyukisik HB, Yanik J. Hydrothermal carbonization and torrefaction of grape pomace: A comparative evaluation. Bioresource Technology. 2014 Jun; 161: 255-262.
• 12. Pohlmann JG, Osorio E, Vilela ACF, Diez MA, Borrego AG. Integrating physicochemical information to follow the transformations of biomass upon torrefaction and low-temperature carbonization. Fuel. 2014 Sep 1; 131: 17-27.
• 13. Soponpongpipat N SD, Sae-Ueng U. Higher heating value prediction of torrefaction char produced from non-woody biomass. Front Energy. 2015; 9(4): 461-471.
• 14. Strandberg M, Olofsson I, Pommer L, Wiklund-Lindstrom S, Aberg K, Nordin A. Effects of temperature and residence time on continuous torrefaction of spruce wood. Fuel Processing Technology. 2015 Jun; 134: 387-398.
• 15. Wannapeera J, Fungtammasan B, Worasuwannarak N. Effects of temperature and holding time during torrefaction on the pyrolysis behaviors of woody biomass. Journal of Analytical and Applied Pyrolysis. 2011 Sep; 92(1): 99-105.
• 16. Wannapeera J, Worasuwannarak N. Upgrading of woody biomass by torrefaction under pressure. Journal of Analytical and Applied Pyrolysis. 2012 Jul; 96: 173-180.
• 17. Yin CY. Prediction of higher heating values of biomass from proximate and ultimate analyses. Fuel. 2011 Mar; 90(3): 1128-1132.
• 18. Ozveren U. An artificial intelligence approach to predict a lower heating value of municipal solid waste. Energy Sources Part a-Recovery Utilization and Environmental Effects. 2016; 38(19): 2906-2913.
• 19. Fausett LV. Fundamentals of neural networks : architectures, algorithms, and applications. Prentice-Hall; 1994 3.
• 20. Heydecker BG, Wu J. Identification of sites for road accident remedial work by Bayesian statistical methods: an example of uncertain inference. Advances in Engineering Software. 2001 Oct-Nov; 32(10-11): 859-869.
• 21. Sun Z, Chen Y, Li XY, Qin XL, Wang HY. A Bayesian regularized artificial neural network for adaptive optics forecasting. Optics Communications. 2017 Jan 1; 382: 519-527.
• 22. Li X, Wang DS. A Sensor Registration Method Using Improved Bayesian Regularization Algorithm. International Joint Conference on Computational Sciences and Optimization, Vol 2, Proceedings. 2009: 195-199.
• 23. Monteiro RVA, Guimaraes GC, Moura FAM, Albertini MRMC, Albertini MK. Estimating photovoltaic power generation: Performance analysis of artificial neural networks, Support Vector Machine and Kalman filter. Electric Power Systems Research. 2017 Feb; 143: 643-656.