Effluent Quality Prediction of Wastewater Treatment Plant Based on Fuzzy-Rough Sets and Artificial Neural Networks

Effluent ammonia-nitrogen (NH3-N), chemical oxygen demand (COD) and total nitrogen (TN) removals are the most common environmental and process performance indicator for all types of wastewater treatment plants (WWTPs). In this paper, a soft computing approach based on the back propagation (BP) neural networks and fuzzy-rough sets (FR-BP) has been applied for forecasting effluent NH3-N, COD and TN concentration of a real WWTP, in which the fuzzy-rough sets theory is employed to perform input selection of neural network which can reduce the influence due to the drawbacks of BP such as low training speed and easily affected by noise and weak interdependency data. The model performance is evaluated with statistical parameters and the simulation results indicates that the FR-BP modeling approach achieves much more accurate predictions as compared with the other traditional modeling approaches.