Grey and neural network prediction of suspended solids and chemical oxygen demand in hospital wastewater treatment plant effluent

Abstract

Grey model (GM) and artificial neural network (ANN) was employed to predict suspended solids (SS) and chemical oxygen demand (COD) in the effluent from sequence batch reactors of a hospital wastewater treatment plant (HWWTP). The results indicated that the minimum mean absolute percentage errors (MAPEs) of 23.14% and 51.73% for SS and COD could be achieved using genetic algorithm ANN (GAANN). The minimum prediction accuracy of 23.14% and 55.11% for SS and COD could be achieved. Contrarily, GM only required a small amount of data and the prediction accuracy was analogous to that of GAANN. In the first type of application, the MAPE values of SS for model prediction using GM (1, N) and GM (1, 2) lay between 23.14% and 26.67%. The MAPE values of COD using GM (1, N) were smaller than those of GM (1, 2). The results showed that the fitness was good for both GM (1, N) and GM (1, 2) to predict SS. However, only GM (1, N) was better for COD prediction as comparing to GM (1, 2). In the second type application, the MAPE values of SS and COD prediction using GM (1, 1) and rolling GM (1, 1) (RGM, i.e., 8 data before the point at which was considered to be predicted were used to construct model) lay between 24–28% and 37–52%, respectively. Furthermore, it was observed that influent pH has affected effluent SS and COD significantly. It suggested that if the influent pH could be adjusted appropriately, a better effluent SS and COD could be obtained.