Abstract:

In order to overcome the nonlinearity and large time delay in the biochemical process of wastewater,this paper proposes a prediction model of effluent quality based on the multiple neural network.In this model,the input space is divided into several subspaces via the subtractive clustering,and the corresponding submodels are established based on neural network in the subspaces.Then,in order to eliminate the severe correlation among the submodels and to improve the accuracy and robustness of the model,the submodels are combined via the principal component regression（PCR）.Moreover,the prediction accuracy of the high measured value is improved by using a modified objective function and the generalization ability of the model is strengthened via the weighted feedback correction.Finally,the proposed method is applied to the prediction of ammonia nitrogen content of the effluent from a wastewater treatment plant,and the results verify the effectiveness of the proposed method.

Key words: neural network, modeling, wastewater treatment, feedback, subtractive clustering, principal component regression