Predicting thermodynamic adhesion energies of membrane fouling in planktonic anammox MBR via backpropagation neural network model,Bioresource Technology

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Predicting thermodynamic adhesion energies of membrane fouling in planktonic anammox MBR via backpropagation neural network model
Bioresource Technology ( IF 9.7 ) Pub Date : 2024-06-18 , DOI: 10.1016/j.biortech.2024.131011
Xiang Cai ₁ , Si Pang ₂ , Meijia Zhang ₃ , Jiaheng Teng ₃ , Hongjun Lin ₃ , Siqing Xia ₄

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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 (), electrostatic double layer (), and Lifshitz–van der Waals () energies were selected as output variables, the training dataset was 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 of fit between the predicted and experimental values of thermodynamic adhesion energy (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 for adhesive fouling behavior in anammox MBRs.

中文翻译：

通过反向传播神经网络模型预测浮游厌氧氨氧化 MBR 中膜污染的热力学粘附能

预测热力学粘附能是减轻膜污染的关键策略。本研究利用反向传播 (BP) 神经网络模型来预测浮游厌氧氨氧化 MBR 中与膜污染相关的热力学粘附能。选择酸碱（）、静电双层（）和Lifshitz-van der Waals（）能量作为输出变量，通过先进的Derjaguin-Landau-Verwey-Overbeek（XDLVO）方法收集训练数据集。优化结果确定“7-10-3”为BP模型的最优网络结构。预测结果表明热力学粘附能的预测值与实验值高度吻合（2≥0.9278），表明本研究模型具有较强的预测能力。总体而言，该研究提出了一种实用的 BP 神经网络模型，用于预测热力学粘附能，显着增强了厌氧氨氧化 MBR 中粘附污垢行为的预测工具。

更新日期：2024-06-18

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