Quantification of interfacial energies associated with membrane fouling in a membrane bioreactor by using BP and GRNN artificial neural networks

Abstract

Interfacial energy between sludge foulants and rough membrane surface critically determines adhesive fouling in membrane bioreactors (MBRs). As a current available method, the advanced extensive Derjaguin-Landau-Verwey-Overbeek (XDLVO) approach cannot efficiently quantify the interfacial energy. In this study, novel methods including back propagation (BP) artificial neural network (ANN) and generalized regression neural network (GRNN) were proposed to quantify the interfacial energy associated with the membrane fouling in an MBR. Different levels of 5 apparent input factors and the resulted interfacial energies were used as training and testing databases for establishment of ANN models. The established BP ANN and GRNN models exhibited high regression coefficients and accuracies, suggesting the high capacity of ANN models to capture the complicated non-linear mapping relations between interfacial energy and various factors. As compared with the advanced XDLVO approach, both BP ANN and GRNN showed remarkably improved quantification efficiency. Meanwhile, BP ANN showed better prediction performance than GRNN model. Case study further demonstrated the robustness and feasibility of BP ANN for interfacial energy quantification. This study provided a new approach to quantify interfacial energy associated with membrane fouling.