Modeling and optimization of ethanol fermentation using Saccharomyces cerevisiae: Response surface methodology and artificial neural network

In this study, the capabilities of response surface methodology (RSM) and artificial neural networks (ANN) for modeling and optimization of ethanol production from glucoseusing Saccharomyces cerevisiae in batch fermentation process were investigated. Effect of three independent variables in a defined range of pH (4.2-5.8), temperature (20-40?C) and glucose concentration (20-60 g/l) on the cell growth and ethanol production was evaluated. Results showed that prediction accuracy of ANN was apparently similar to RSM. At optimum condition of temperature (32?C), pH (5.2) and glucose concentration (50 g/l) suggested by the statistical methods, the maximum cell dry weight and ethanol concentration obtained from RSM were 12.06 and 16.2 g/l whereas experimental values were 12.09 and 16.53 g/l, respectively. The present study showed that using ANN as fitness function, the maximum cell dry weight and ethanol concentration were 12.05 and 16.16 g/l, respectively. Also, the coefficients of determination for biomass and ethanol concentration obtained from RSM were 0.9965 and 0.9853 and from ANN were 0.9975 and 0.9936, respectively. The process parameters optimization was successfully conducted using RSM and ANN; however prediction by ANN was slightly more precise than RSM. Based on experimental data maximum yield of ethanol production of 0.5 g ethanol/g substrate (97 % of theoretical yield) was obtained.