Medium optimization for ethanol production with Clostridium autoethanogenum with carbon monoxide as sole carbon source

被引：76

作者：

Guo, Ying ^{[1
,2
]}

Xu, Jingliang ^{[1
]}

Zhang, Yu ^{[1
,2
]}

Xu, Huijuan ^{[1
]}

Yuan, Zhenhong ^{[1
]}

Li, Dong ^{[1
,2
]}

机构：

[1] Chinese Acad Sci, Guangzhou Inst Energy Convers, Key Lab Renewable Energy & Gas Hydrate, Guangzhou 510640, Peoples R China

[2] Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China

来源：

BIORESOURCE TECHNOLOGY | 2010年 / 101卷 / 22期

基金：

国家高技术研究发展计划(863计划);

关键词：

Syngas fermentation; Clostridium autoethanogenum; Response surface methodology; Artificial neural network; Genetic algorithm; ARTIFICIAL NEURAL-NETWORK; SYNTHESIS GAS; BIOLOGICAL PRODUCTION; GENETIC ALGORITHM; SP-NOV; FERMENTATION; BIOMASS; LJUNGDAHLII; GROWTH; FRUIT;

D O I：

10.1016/j.biortech.2010.06.072

中图分类号：

S2 [农业工程];

学科分类号：

0828 ;

摘要：

Plackett-Burman and central composite designs were applied to optimize the medium for ethanol production by Clostridium autoethanogenum with CO as sole carbon source, and a medium containing (g/L): NaCl 1.0, KH2PO4 0.1, CaCl2 0.02, yeast extract 0.15. MgSO4 0.116, NH4Cl 1.694 and pH 4.74 was found optimal. The optimum ethanol yields predicted by response surface methodology (RSM) and an artificial neural network-genetic algorithm (ANN-GA) were 247.48 and 261.48 mg/L, respectively. These values are similar to those obtained experimentally under the optimal conditions suggested by the statistical methods (254.26 and 259.64 mg/L). The fitness of the ANN-GA model was higher than that of the RSM model. The yields obtained substantially exceed those previously reported (60-70 mg/L) with this organism. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：8784 / 8789

页数：6

共 33 条

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