Comparative Study of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) on Optimization of Ethanol Production from Sawdust

Abstract

This work focused on optimization of production of ethanol from saw dust using two empirical methods, the ANN and the RSM. It further investigated the modeling and optimization efficiencies of RSM and ANN in separate hydrolysis and fermentation of sawdust for ethanol production. Box - Behnken Design (BBD) was used to generate 17 individual experiments which were carried out, RSM and Genetic Algorithm (GA) of ANN which were used to optimize the production which was then compared. The optimum concentrations of ethanol yield predicted were 56.968 wt. % and 57. 387263 wt. % for RSM and ANN models respectively. R2 value obtained for ANN model was 0.9989 while R2 value of 0.9046 was obtained for RSM model. The Root Mean Square Error (RMSE) value for ANN was found to be 0.143 while the RMSE value for RSM was 2.17. It showed that ANN had relatively higher predictive model ability and thus shows to be a better optimization tool for the ethanol from saw dust compared to RSM which also a good modelling tool.