Application of radial basis function artificial neural network to quantify interfacial energies related to membrane fouling in a membrane bioreactor.

Efficient quantification of interfacial energy related with membrane fouling represents the primary interest in membrane bioreactors (MBRs) as interfacial energy determines foulant layer formation. In this study, radial basis function (RBF) artificial neural networks (ANNs) with five related factors as input variables were applied to quantify interfacial energy with randomly rough membrane surface. It was found that, RBF ANNs could well capture the complex non-linear relationships between the related factors and interfacial energy. RBF ANN quantification showed high regression coefficient and accuracy, suggesting its high capacity to quantify interfacial energy. Compared to at least one-week time consumption of the advanced extensive Derjaguin-Landau-Verwey-Overbeek (XDLVO) approach, quantification by RBF ANNs only took several seconds for a same case, indicating the high efficiency of RBF ANNs. Moreover, the abilities of RBF ANNs can be further improved. The robust RBF ANNs proposed paved a new way to study membrane fouling in MBRs.