Forecasting effluent quality of an industry wastewater treatment plant by evolutionary grey dynamic model

The application of a prediction model is a commendable exercise to evaluate a facility's performance and achieve better quality control in the operation of wastewater treatment plants. This paper proposes a model which integrates grey dynamic modeling and genetic algorithm to predict accurately the effluent quality of an industrial wastewater treatment plant located in southern Taiwan. Model parameters, variables and structures are determined endogenously to minimize errors between observed and predicted values. Modeling feasibility has been proved by using data compared with Monte Carlo simulation and artificial neural network approaches. The results show that the prediction of our proposed model is sensitive to the joint effect of suspended solids and F/M.