期刊
BIORESOURCE TECHNOLOGY
卷 101, 期 22, 页码 8784-8789出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.biortech.2010.06.072
关键词
Syngas fermentation; Clostridium autoethanogenum; Response surface methodology; Artificial neural network; Genetic algorithm
资金
- 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]
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.
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