Prediction of interfacial interactions related with membrane fouling in a membrane bioreactor based on radial basis function artificial neural network (ANN). (June 2019)

• Record Type: Journal Article
• Authors: Zhao, Zhitao; Lou, Yang; Chen, Yifeng; Lin, Hongjun; Li, Renjie; Yu, Genying
• Is Part Of: Bioresource technology. Volume 282(2019)
• Page Start: 262
• Page End: 268
• Publication Date: 2019-06
• Subjects: Membrane fouling -- Membrane bioreactor -- Interface interaction -- XDLVO theory -- Artificial neural network; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2019.03.044
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: An artificial neural network (ANN) to predict the interfacial interactions was proposed. Interfacial interactions with randomly rough membrane surface can be quantified. Radial basis function (RBF) ANN method showed the high efficiency to predict fouling. The proposed RBF ANN method had broad application prospects in fouling research. This study demonstrated breakthrough of the fundamental method regarding fouling. Abstract: It is of great importance to propose effective methods to quantify interfacial interaction since it directly determines foulant adhesion and membrane fouling process in membrane bioreactors (MBRs). This study developed a radial basis function (RBF) artificial neural network (ANN) to predict the interfacial interactions with randomly rough membrane surface. The interaction data quantified by the advanced extended Derjaguin–Landau–Verwey–Overbeek (XDLVO) approach were used as the training samples for the RBF networks. It was found that, the computing time consumption for the RBF network prediction was only about 1/50 of that for the advanced XDLVO approach under same conditions, indicating the high efficiency of the RBF ANN method. Meanwhile, the calculation accuracy of the method was acceptable to get reliable results. This study demonstrated the breakthrough of the fundamental methodology related with membrane fouling. The proposed RBF ANN method has broad application prospects in membrane fouling and interface behavior research.