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

Abstract

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 (20-40ºC) 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

Highly selective carbon nanotube‐coupled graphene oxide‐incorporated polydimethylsiloxane membrane for pervaporative membrane bioreactor ethanol production

“…Also, the medium used in the reactor was composed of 50 g L −1 glucose, 0.45 g L −1 NH 4 Cl and 1 g L −1 yeast extract. A buffer solution of potassium hydrogen phthalate (0.1 mol L −1 ) and sodium hydroxide (0.1 mol L −1 ) was prepared to set the pH value to 5.2 . After sterilization, this medium was inoculated with 5% of seed culture and prepared to begin the experiment at 32 °C.…”

“…A buffer solution of potassium hydrogen phthalate (0.1 mol L −1 ) and sodium hydroxide (0.1 mol L −1 ) was prepared to set the pH value to 5.2. 3 After sterilization, this medium was inoculated with 5% of seed culture and prepared to begin the experiment at 32°C. Batch fermentation combined with pervaporation was performed in the designed PVMBR (600 mL).…”

<strong></strong>Comparison of Response Surface Methodology and Hybrid-Training Approach of Artificial Neural Network in Modeling the Properties of Concrete Containing Steel Fiber Extracted from Waste Tires

“…They concluded that the application of either RSM or ANN modeling techniques is practical approach with a promising future depending on the objectives to be achieved. Esfahanian et al [24] modeled and optimized ethanol fermentation using saccharomyces cerevisiae. They observed that the prediction by ANN was slightly more precise than RSM based on experimental data.…”

The effects of passivation parameters on pitting potential of biomedical stainless steel

“…During the experiments, the temperature was maintained at 37±1 C (typical body temperature). The composition and instruction for preparation of the Hank's solution are described elsewhere [23].…”