![]() 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. 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. 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. 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. 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. ![]()
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