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.
: 20 Ekim 2017
|Vancouver||Özveren U , Di̇lmac O , Mert M , Karaca Albayrak F . Investigation of the Chemical Exergy of Torrefied Lignocellulosic Fuels using Artificial Neural Networks. Journal of the Turkish Chemical Society Section B: Chemical Engineering. 2017; 1(Sp. is. 1): 69-76.|