Investigation of the Chemical Exergy of Torrefied Lignocellulosic Fuels using Artificial Neural Networks

Authors : Ugur ÖZVEREN, Omer Faruk DİLMAC, Mehmet Selçuk MERT, Fatma KARACA ALBAYRAK

Pages : 69-76

View : 17 | Download : 11

Publication Date : 2017-10-20

Article Type : Research Paper

Abstract :Normal 0 21 false false false TR X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:`Table Normal`; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:``; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:`Calibri`,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:`Times New Roman`; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;} 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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(R 2 =0,9999); and its robustness and capability to compute the chemical exergy of any torrefied lignocellulosic fuels from its proximate analysis and heating value.
Keywords : Lignocellulosic fuels, Chemical exergy, Proximate analysis, Neural networks