ANFIS Based Effluent pH Quality Prediction Model for an Activated Sludge Process

Abstract

Activated sludge process is the most efficient technique used for municipal wastewater treatment plants. However, a pH value outside the limit of 6-9 could inhibit the activities of microorganisms responsible for treating the wastewater, and low pH value may cause damage to the treatment system. Therefore, prediction of pH value is essential for smooth and trouble-free operation of the process. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) model for effluent pH quality prediction in the process. For comparison, artificial neural network is used. The model validation is achieved through use of full-scale data from the domestic wastewater treatment plant in Kuala Lumpur, Malaysia. Simulation results indicate that the ANFIS model predictions were highly accurate having the root mean square error (RMSE) of 0.18250, mean absolute percentage deviation (MAPD) of 9.482% and the correlation coefficient (R) of 0.72706. The proposed model is efficient and valuable tool for the activated sludge wastewater treatment process.