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.
Corrigendum: Augmenting Pentose Utilization and Ethanol Production of Native Saccharomyces Cerevisiae LN Using Medium Engineering and Response Surface Methodology
