Predicting performance of grey and neural network in industrial effluent using online monitoring parameters

Abstract

Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids (SS eff), chemical oxygen demand (COD eff) and pH eff in the effluent from conventional activated process of an industrial wastewater treatment plant using simple online monitoring parameters (pH in the equalization pond effluent; pH, temperature, and dissolved oxygen in the aeration tank). The results indicated that the minimum mean absolute percentage errors of 20.79, 6.09 and 0.71% for SS eff, COD eff and pH eff, respectively, could be achieved using different types of GMs. GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. According to the results, the online monitoring parameters could be applied on the prediction of effluent quality. It also revealed that GM could predict the industrial effluent variation as its effluent data was insufficient.