文献类型：期刊文献

英文题名：Quantification of interfacial interaction related with adhesive membrane fouling by genetic algorithm back propagation (GABP) neural network

作者：Li, Bowen[1];Shen, Liguo[1];Zhao, Ying[2];Yu, Wei[1];Lin, Hongjun[1];Chen, Cheng[1];Li, Yingbo[1];Zeng, Qianqian[1]

第一作者：Li, Bowen

通讯作者：Shen, LG[1];Lin, HJ[1];Zhao, Y[2]

机构：[1]Zhejiang Normal Univ, Coll Geog & Environm Sci, Jinhua 321004, Peoples R China;[2]Beijing Union Univ, Teachers Coll, 5 Waiguanxiejie St, Beijing 100011, Peoples R China

第一机构：Zhejiang Normal Univ, Coll Geog & Environm Sci, Jinhua 321004, Peoples R China

通讯机构：[1]corresponding author), Zhejiang Normal Univ, Coll Geog & Environm Sci, Jinhua 321004, Peoples R China;[2]corresponding author), Beijing Union Univ, Teachers Coll, 5 Waiguanxiejie St, Beijing 100011, Peoples R China.|[1141711]北京联合大学师范学院;[11417]北京联合大学;

年份：2023

卷号：640

起止页码：110-120

外文期刊名：JOURNAL OF COLLOID AND INTERFACE SCIENCE

收录：;EI(收录号：20230913659238);Scopus(收录号：2-s2.0-85149059192);WOS:【SCI-EXPANDED(收录号:WOS:000948478100001)】；

基金：Financial support of Zhejiang Provincial Outstanding Youth Science Foundation (No. LR22E080007) , National Natural Science Foundation of China (No. 52070170) , and Key Research and Devel- opment Program of Zhejiang Province (No. 2022C03069) are highly appreciated.

语种：英文

外文关键词：Artificial neural network; Genetic algorithm; Advanced XDLVO theory; Interfacial interaction; Membrane fouling

摘要：Since adhesive membrane fouling is critically determined by the interfacial interaction between a foulant and a rough membrane surface, efficient quantification of the interfacial interaction is critically important for adhesive membrane fouling mitigation. As a current available method, the advanced extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory involves complicated rigorous thermodynamic equations and massive amounts of computation, restricting its application. To solve this problem, artifi-cial intelligence (AI) visualization technology was used to analyze the existing literature, and the genetic algorithm back propagation (GABP) artificial neural network (ANN) was employed to simplify thermody-namic calculation. The results showed that GABP ANN with 5 neurons could obtain reliable prediction performance in seconds, versus several hours or even days time-consuming by the advanced XDLVO the-ory. Moreover, the regression coefficient (R) of GABP reached 0.9999, and the error between the predic-tion results and the simulation results was less than 0.01%, indicating feasibility of the GABP ANN technique for quantification of interfacial interaction related with adhesive membrane fouling. This work provided a novel strategy to efficiently optimize the thermodynamic prediction of adhesive membrane fouling, beneficial for better understanding and control of adhesive membrane fouling.(c) 2023 Elsevier Inc. All rights reserved.