MODELING AND OPTIMIZATION OF ETHANOL FERMENTATION USING Saccharomyces cerevisiae: RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORK

被引:35
作者
Esfahanian, Mehri [1 ]
Nikzad, Maryam [1 ]
Najafpour, Ghasem [1 ]
Ghoreyshi, Ali Asghar [1 ]
机构
[1] Noushirvani Univ Technol, Fac Chem Engn, Babol Sar, Iran
关键词
artificial neural network; ethanol fermentation; response surface methodology; Saccharomyces cerevlsiae; ethanol yield; PLANT-CELL CULTURES; BIOMASS ESTIMATION; CELLULASE; ANN;
D O I
10.2298/CICEQ120210058E
中图分类号
O69 [应用化学];
学科分类号
081704 ;
摘要
In this study, the capabilities of response surface methodology (RSM) and artificial neural networks (ANN) for modeling and optimization of ethanol production from glucose using Saccharomyces cerevisiae in batch fermentation process were investigated. The effects of three independent variables in a defined range of pH (4.2-5.8), temperature (20-40 degrees C) and glucose concentration (20-60 g/l) on the cell growth and ethanol production were evaluated. The results showed that the prediction accuracy of ANN was apparently similar to RSM. At optimum conditions of temperature (32 degrees 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 RSNI 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 a 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 RSNI were 0.9965 and 0.9853 and from ANN were 0.9975 and 0.9936, respectively. The process parameters optimization was successfully conducted using RSNI and ANN; however, prediction by ANN was slightly more precise than RSM. Based on experimental data, the maximum yield of ethanol production of 0.5 g ethanol/g substrate (97% of theoretical yield) was obtained.
引用
收藏
页码:241 / 252
页数:12
相关论文
共 23 条
[1]   BIOMASS ESTIMATION IN PLANT-CELL CULTURES - A NEURAL-NETWORK APPROACH [J].
ALBIOL, J ;
CAMPMAJO, C ;
CASAS, C ;
POCH, M .
BIOTECHNOLOGY PROGRESS, 1995, 11 (01) :88-92
[2]   BIOMASS ESTIMATION IN PLANT-CELL CULTURES USING AN EXTENDED KALMAN FILTER [J].
ALBIOL, J ;
ROBUSTE, J ;
CASAS, C ;
POCH, M .
BIOTECHNOLOGY PROGRESS, 1993, 9 (02) :174-178
[3]   Optimization of critical medium components using response surface methodology for ethanol production from cellulosic biomass by Clostridium thermocellum SS19 [J].
Balusu, R ;
Paduru, RR ;
Kuravi, SK ;
Seenayya, G ;
Reddy, G .
PROCESS BIOCHEMISTRY, 2005, 40 (09) :3025-3030
[4]   Modeling and optimization III: Reaction rate estimation using artificial neural network (ANN) without a kinetic model [J].
Bas, Deniz ;
Dudak, Fahriye Ceyda ;
Boyaci, Ismail Hakki .
JOURNAL OF FOOD ENGINEERING, 2007, 79 (02) :622-628
[5]  
BASKAR G, 2008, INT J CHEM BIOMOLECU, V1, P156
[6]   Production of poly-3-hydroxybutyrate by Cupriavidus necator from corn syrup: statistical modeling and optimization of biomass yield and volumetric productivity [J].
Daneshi, Ali ;
Younesi, Habibollah ;
Ghasempouri, Seyd Mahmood ;
Sharifzadeh, Mazyar .
JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY, 2010, 85 (11) :1528-1539
[7]  
Ezhumalai S., 2010, INT J RECENT SCI RES, V5, P125
[8]  
Ezhumalai S, 2010, BIORESOURCES, V5, P1879
[9]   Modeling of wheat soaking using two artificial neural networks (MLP and RBF) [J].
Kashaninejad, M. ;
Dehghani, A. A. ;
Kashiri, M. .
JOURNAL OF FOOD ENGINEERING, 2009, 91 (04) :602-607
[10]   Modeling the process and costs of fuel ethanol production by the corn dry-grind process [J].
Kwiatkowski, JR ;
McAloon, AJ ;
Taylor, F ;
Johnston, DB .
INDUSTRIAL CROPS AND PRODUCTS, 2006, 23 (03) :288-296