Ethanol Production by Saccharomyces cerevisiae Grown in Sugarcane Blackstrap Molasses Through a Fed-Batch Process: Optimization by Response Surface Methodology

2003 ◽  
Vol 110 (3) ◽  
pp. 151-164 ◽  
Author(s):  
João C. M. Carvalho ◽  
Michele Vitolo ◽  
Sunao Sato ◽  
Eugênio Aquarone
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Response Surface Methodology ◽  
Ethanol Production ◽  
Response Surface ◽  
Process Optimization ◽  
Batch Process ◽  
Fed Batch

Improving ethanol production and viability of Saccharomyces cerevisiae by a vitamin feeding strategy during fed-batch process

2002 ◽  
Vol 60 (1-2) ◽  
pp. 67-72 ◽  
Author(s):  
Alfenore S. ◽  
Molina-Jouve C. ◽  
Guillouet S. ◽  
Uribelarrea J.-L. ◽  
Goma G. ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Ethanol Production ◽  
Feeding Strategy ◽  
Batch Process ◽  
Fed Batch

scholarly journals Optimal growth of Saccharomyces cerevisiae (PTCC 24860) on pretreated molasses for ethanol production: Application of response surface methodology

2010 ◽  
Vol 16 (2) ◽  
pp. 199-206 ◽  
Author(s):  
Hoda Shafaghat ◽  
Ghasem Najafpour ◽  
Sirous Rezaei ◽  
Mazyar Sharifzadeh
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Response Surface Methodology ◽  
Cell Growth ◽  
Sugar Beet ◽  
Ethanol Production ◽  
Response Surface ◽  
Incubation Time ◽  
Influence Of Process Parameters ◽  
Beet Molasses ◽  
Sugar Beet Molasses

Saccharomyces cerevisiae (PTCC 24860) growth on pretreated sugar beet molasses was optimized via statistical approach. In order to liberate all monomeric sugars, pretreated sugar beet molasses with dilute acid was obtained. The influence of process parameters such as sugar concentration, nitrogen source, pH and incubation time on cell growth were investigated by design expert software with application of central composite design (CCD) under response surface methodology (RSM). The optimal culture conditions were pH of 5.3, incubation time of 24 h and medium composition of 35 g reduced sugars, 1.5 g NH4Cl and 1 g yeast extract per liter of the media. At optimal cell growth conditions and incubation time of 12 h, maximum ethanol production of 14.87 g/L was obtained.

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

2013 ◽  
Vol 19 (2) ◽  
pp. 241-252 ◽  
Author(s):  
Mehri Esfahanian ◽  
Maryam Nikzad ◽  
Ghasem Najafpour ◽  
Asghar Ghoreyshi
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Response Surface Methodology ◽  
Ethanol Production ◽  
Response Surface ◽  
Glucose Concentration ◽  
Ethanol Concentration ◽  
Dry Weight ◽  
Modeling And Optimization ◽  
Maximum Cell ◽  
Artificial Neural

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.

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