膜科学与技术

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Application of artificial neural networks in quantifying interface interaction energy associated with membrane fouling in a membrane bioreactor

Authors： CHEN Yifeng, SHEN Liguo, LIN Hongjun

Units： College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, 321004, China

KeyWords： artificial neural network; membrane bioreactor; membrane fouling; interfacial energy

ClassificationCode：TQ028.5; X703.1

year,volume(issue):pagination： 2020,40(3):47-54

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 extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) approach cannot efficiently quantify the interfacial energy. In this study, novel methods including radial basis function (RBF) artificial neural network (ANN), back propagation (BP) ANN and generalized regression neural network (GRNN) were proposed to quantify the interfacial energy associated with the membrane fouling in an MBR. The prediction results of RBF ANN, BP ANN and GRNN have high regression coefficients and accuracies, suggesting their high capacity to capture the complicated non-linear mapping relations between interfacial energy and various factors. As compared with the advanced XDLVO approach, both RBF ANN, BP ANN and GRNN showed remarkably improved quantification efficiency. Meanwhile, BP ANN showed better prediction performance than RBF ANN and 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.

Funds：

国家自然科学基金资助项目（No. 51978628）

AuthorIntro：

第一作者简介：陈镒锋（1995－），男，浙江海宁人，硕士研究生，主要研究方向为膜污染控制．E-mail：yfchen@zjnu.edu.cn *通讯作者，E-mail：hjlin@zjnu.cn

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