Prediction and optimization of ethanol concentration in biofuel production using fuzzy neural network

In recent years,producing economical biofuels especially bio-ethanol from lignocellulosic materials has been widely considered. fermentation is an important step in ethanol production process. fermentation process is completely nonlinear and depends on some parameters such as temperature,sugar content,and ph. one of the difficulties in producing biomass is finding the optimum point of the interrelated parameters in the fermentation step. in this study,an elaborate prediction neuro-fuzzy model was built to predict the bio-ethanol production from corn stover. also,particle swarm optimization (pso) method was used to optimize the three studied parameters: temperature,glucose content,and fermentation time. the attained correlation coefficient (0.99),and root mean square error (0.637) for model validation show the reliability of the model. optimization of the model shows the optimum fermentation time and required temperature quantities,69.39hours and 34.50 °c,respectively. the good result for anfis modeling on fermentation process in bio-ethanol production from corn stover shows that this method can be used to investigate more about other biomass lignocellulos sources. © 2016,penerbit utm press. all rights reserved.