Predicting thermodynamic adhesion energies of membrane fouling planktonic anammox MBR via backpropagation neural network model

文献类型： 外文期刊

第一作者： Cai, Xiang

作者： Cai, Xiang;Zhang, Meijia;Teng, Jiaheng;Lin, Hongjun;Cai, Xiang;Pang, Si;Xia, Siqing;Cai, Xiang;Xia, Siqing;Pang, Si

作者机构：

关键词： Membrane fouling; anammox MBR; Adhesion energies; Prediction model; Backpropagation neural network

期刊名称：BIORESOURCE TECHNOLOGY （ 影响因子：9.0； 五年影响因子：9.5 ）

ISSN： 0960-8524

年卷期： 2024 年 406 卷

页码：

收录情况： SCI

摘要： Predicting thermodynamic adhesion energies was a critical strategy for mitigating membrane fouling. This study utilized a backpropagation (BP) neural network model to predict the thermodynamic adhesion energies associated with membrane fouling in a planktonic anammox MBR. Acid-base (Delta GAB), Delta G AB ), electrostatic double layer (Delta GEL), Delta G EL ), and Lifshitz-van der Waals ( Delta G LW ) energies were selected as output variables, the training dataset collected by the advanced Derjaguin-Landau-Verwey-Overbeek (XDLVO) method. Optimization results identified "7-10-3 '' '' as the optimal network structure for the BP model. The prediction results demonstrated a high degree fit between the predicted and experimental values of thermodynamic adhesion energy (R2 R 2 >= 0.9278), indicating a robust predictive capability of the model in this study. Overall, the study presented a practical BP neural network model for predicting thermodynamic adhesion energies, significantly enhancing the prediction tool adhesive fouling behavior in anammox MBRs.

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