中文摘要：

二种人工智能技术，人工的神经网络和基因算法，被使用为改进亚硝酸根氧化的亚硝酸根氧化率优化发酵媒介细菌。实验与基因算法获得的中等部件的作文被进行，并且试验性的数据被用来造 BP (背繁殖) 神经网络模型。当输入向量，和亚硝酸根氧化率被用作模型的产量向量，六个中等部件的集中被使用。BP 神经网络模型被用作基因算法的客观函数为最大的亚硝酸根氧化率发现最佳中等作文。最大的亚硝酸根氧化率是 0.952 g NO2-N

英文摘要：

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 2 NO-2-N·（g MLSS）-1·d-1 , obtained at the genetic algorithm optimized concentration of medium components （g·L-1 ）： NaCl 0.58, MgSO 4 ·7H 2 O 0.14, FeSO 4 ·7H 2 O 0.141, KH 2 PO 4 0.8485, NaNO 2 2.52, and NaHCO 3 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.