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 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.