The simulation and prediction of TN in wastewater treatment effluent using BP neural network and ARIMA model

Total nitrogen in effluent is one of the critical indicators for evaluating the performance of wastewater treatment plants. A BP neural network model was developed to simulate the present nitrogen removal system for wastewater treatment, and an autoregressive integrated moving average (ARIMA) model was creatively applied to realize the short-term prediction of future effluent. The results showed that the simulation average relative error of BP model on training set was 15.9%, and that on test set was 16.5%,which revealed that the stability of model prediction was poor. The average error of the ARIMA model for predicting the total nitrogen value in the coming week was around 4.41%, which showed high prediction accuracy. The combination of the two models could help fast and efficient on-line detection of wastewater treatment plants.