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

• Published in: Journal of colloid and interface science 2023-06, Vol.640, p.110-120
• Main Authors: Li, Bowen, Shen, Liguo, Zhao, Ying, Yu, Wei, Lin, Hongjun, Chen, Cheng, Li, Yingbo, Zeng, Qianqian
• Format: Article
• Language: English
• Subjects: Advanced XDLVO theory; Artificial neural network; Genetic algorithm; Interfacial interaction; Membrane fouling
• ISSN: 0021-9797; 1095-7103
• DOI: 10.1016/j.jcis.2023.02.030

[Display omitted] •An artificial neural network (ANN) was firstly optimized by genetic algorithm (GA).•The structure of artificial neural network (ANN) was optimized by back propagation (BP).•GABP neural network gave more accurate results in membrane fouling prediction.•The regression coefficient (R) of GABP reached to 0.99990.•The error between the prediction results and the simulation results was less than 0.01%. 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, artificial 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 thermodynamic 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 theory. Moreover, the regression coefficient (R) of GABP reached 0.9999, and the error between the prediction 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.