A hybrid genetic--neural algorithm for modeling the biodegradation process of DnBP in AAO system

Abstract

A hybrid artificial neural network - genetic algorithm numerical technique was successfully developed to model, and to simulate the biodegradation process of di-n-butyl phthalate in an anaerobic/anoxic/oxic (AAO) system. The fate of DnBP was investigated, and a removal kinetic model including sorption and biodegradation was formulated. To correlate the experimental data with available models or some modified empirical equations, the steady state model equations describing the biodegradation process have been solved using genetic algorithm (GA) and artificial neural network (ANN) from the water quality characteristic parameters. Compared with the kinetic model, the performance of the GA-ANN for modeling the DnBP was found to be more impressive. The results show that the predicted values well fit measured concentrations, which was also supported by the relatively low RMSE (0.2724), MAPE (3.6137) and MSE (0.0742)and very high R (0.9859) values, and which illustrates the GA-ANN model predicting effluent DnBP more accurately than the mechanism model forecasting.