SCI和EI收录∣中国化工学会会刊

Chinese Journal of Chemical Engineering ›› 2012, Vol. 20 ›› Issue (5): 950-957.

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Optimization of Fermentation Media for Enhancing Nitrite-oxidizing Activity by Artificial Neural Network Coupling Genetic Algorithm*

罗剑飞, 林炜铁, 蔡小龙, 李敬源   

  1. School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510006, China
  • 收稿日期:2011-04-19 修回日期:2011-09-24 出版日期:2012-10-28 发布日期:2012-11-06
  • 通讯作者: LIN Weitie,E-mail:weitie@21cn.com
  • 基金资助:
    Supported by the National Natural Science Foundation of China (21076090)

Optimization of Fermentation Media for Enhancing Nitrite-oxidizing Activity by Artificial Neural Network Coupling Genetic Algorithm*

LUO Jianfei, LIN Weitie, CAI Xiaolong, LI Jingyuan   

  1. School of Bioscience and Bioengineering, South China University of Technology, Guangzhou 510006, China
  • Received:2011-04-19 Revised:2011-09-24 Online:2012-10-28 Published:2012-11-06
  • Supported by:
    Supported by the National Natural Science Foundation of China (21076090)

摘要: Two artificial intelligence techniques, artificial neural network and genetic algorithm, were applied to optimize the fermentation medium for improving the nitrite oxidization rate of nitrite oxidizing bacteria. Experiments were conducted with the composition of medium components obtained by genetic algorithm, and the experimental data were used to build a BP (back propagation) neural network model. The concentrations of six medium components were used as input vectors, and the nitrite oxidization rate was used as output vector of the model. The BP neural network model was used as the objective function of genetic algorithm to find the optimum medium composition for the maximum nitrite oxidization rate. The maximum nitrite oxidization rate was 0.952 g NO2--N·(g MLSS)-1·d-1, obtained at the genetic algorithm optimized concentration of medium components (g·L-1): NaCl 0.58, MgSO4·7H2O 0.14, FeSO4·7H2O 0.141, KH2PO4 0.8485, NaNO2 2.52, and NaHCO3 3.613. Validation experiments suggest that the experimental results are consistent with the best result predicted by the model. A scale-up experiment shows that the nitrite degraded completely after 34 h when cultured in the optimum medium, which is 10 h less than that cultured in the initial medium.

关键词: BP neural network, genetic algorithm, optimization, nitrite oxidization rate, nitrite-oxidizing bacteria

Abstract: Two artificial intelligence techniques, artificial neural network and genetic algorithm, were applied to optimize the fermentation medium for improving the nitrite oxidization rate of nitrite oxidizing bacteria. Experiments were conducted with the composition of medium components obtained by genetic algorithm, and the experimental data were used to build a BP (back propagation) neural network model. The concentrations of six medium components were used as input vectors, and the nitrite oxidization rate was used as output vector of the model. The BP neural network model was used as the objective function of genetic algorithm to find the optimum medium composition for the maximum nitrite oxidization rate. The maximum nitrite oxidization rate was 0.952 g NO2--N·(g MLSS)-1·d-1, obtained at the genetic algorithm optimized concentration of medium components (g·L-1): NaCl 0.58, MgSO4·7H2O 0.14, FeSO4·7H2O 0.141, KH2PO4 0.8485, NaNO2 2.52, and NaHCO3 3.613. Validation experiments suggest that the experimental results are consistent with the best result predicted by the model. A scale-up experiment shows that the nitrite degraded completely after 34 h when cultured in the optimum medium, which is 10 h less than that cultured in the initial medium.

Key words: BP neural network, genetic algorithm, optimization, nitrite oxidization rate, nitrite-oxidizing bacteria