Application of neural network in modeling of activated sludge wastewater treatment process

This paper present a prediction model for three different objection (the airflow rate, the carbonaceous biochemical oxygen demand (CBOD) of the effluent, and the total suspend solids (TSS) of the effluent). The model is built by the MLP neural network. The accuracy of the prediction result of MLP neural network is compared with the accuracy of the result of traditional stational autoregressive model(AR). The conclusion is that the percentage error prediction model of the MLP neural network (PE), the fractional deviation (FB), normalized mean square error (NMSE), the mean absolute error (MAE) and mean square error (MSE) prediction model of evaluation index is better than the AR model. In other words, the prediction model based on MLP neural network provides a reliable basis for reducing the energy consumption in the activated sludge process of industrial waste water treatment and further improving its effect on the treatment of industrial waste water.