English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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

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