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Medium optimization for ethanol production with Clostridium autoethanogenum with carbon monoxide as sole carbon source
Guo, Ying1,2; Xu, Jingliang1; Zhang, Yu1,2; Xu, Huijuan1; Yuan, Zhenhong1; Li, Dong1,2
2010-11-01
发表期刊BIORESOURCE TECHNOLOGY
ISSN0960-8524
卷号101期号:22页码:8784-8789
产权排序[Guo, Ying; Xu, Jingliang; Zhang, Yu; Xu, Huijuan; Yuan, Zhenhong; Li, Dong] Chinese Acad Sci, Guangzhou Inst Energy Convers, Key Lab Renewable Energy & Gas Hydrate, Guangzhou 510640, Peoples R China; [Guo, Ying; Zhang, Yu; Li, Dong] Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China
通讯作者xjl@ms.giec.ac.cn ; yuanzh@ms.giec.ac.cn
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, KH(2)PO(4) 0.1, CaCl(2) 0.02, yeast extract 0.15. MgSO(4) 0.116, NH(4)Cl 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.
文章类型Article
其他摘要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, KH(2)PO(4) 0.1, CaCl(2) 0.02, yeast extract 0.15. MgSO(4) 0.116, NH(4)Cl 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.
关键词Syngas Fermentation Clostridium Autoethanogenum Response Surface Methodology Artificial Neural Network Genetic Algorithm
学科领域Agriculture ; Biotechnology & Applied Microbiology ; Energy & Fuels
WOS标题词Science & Technology ; Life Sciences & Biomedicine ; Technology
DOI10.1016/j.biortech.2010.06.072
研究领域[WOS]Agriculture ; Biotechnology & Applied Microbiology ; Energy & Fuels
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关键词[WOS]ARTIFICIAL NEURAL-NETWORKS ; GENETIC ALGORITHMS ; SYNTHESIS GAS ; SP-NOV ; BIOLOGICAL PRODUCTION ; FERMENTATION ; BIOMASS ; LJUNGDAHLII ; GROWTH ; CELLS
收录类别SCI
语种英语
项目资助者National High Technology Research and Development Program of China [2007AA05Z406]; Chinese Academy of Sciences [KGCX2-YW-335, KSCX-YW-11-A3, KSCX2-YW-G-075-09]
WOS类目Agricultural Engineering ; Biotechnology & Applied Microbiology ; Energy & Fuels
WOS记录号WOS:000281262900044
引用统计
被引频次:40[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.giec.ac.cn/handle/344007/8472
专题中国科学院广州能源研究所
生物质能源生化转化实验室
作者单位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
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Guo, Ying,Xu, Jingliang,Zhang, Yu,et al. Medium optimization for ethanol production with Clostridium autoethanogenum with carbon monoxide as sole carbon source[J]. BIORESOURCE TECHNOLOGY,2010,101(22):8784-8789.
APA Guo, Ying,Xu, Jingliang,Zhang, Yu,Xu, Huijuan,Yuan, Zhenhong,&Li, Dong.(2010).Medium optimization for ethanol production with Clostridium autoethanogenum with carbon monoxide as sole carbon source.BIORESOURCE TECHNOLOGY,101(22),8784-8789.
MLA Guo, Ying,et al."Medium optimization for ethanol production with Clostridium autoethanogenum with carbon monoxide as sole carbon source".BIORESOURCE TECHNOLOGY 101.22(2010):8784-8789.
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