College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China.
State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; Institute of Eco-Environment and Plant Protection, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China.
Bioresour Technol. 2024 Aug;406:131011. doi: 10.1016/j.biortech.2024.131011. Epub 2024 Jun 18.
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 (ΔG), electrostatic double layer (ΔG), and Lifshitz-van der Waals (ΔG) 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 (R2 ≥ 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.
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）神经网络模型预测了在浮游 Anammox MBR 中与膜污染相关的热力学粘附能。酸碱（ΔG）、静电双电层（ΔG）和 Lifshitz-van der Waals（ΔG）能被选为输出变量，训练数据集通过先进的 Derjaguin-Landau-Verwey-Overbeek（XDLVO）方法收集。优化结果确定“7-10-3”作为 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之间具有高度的拟合度（R2≥0.9278），表明该模型在本研究中具有强大的预测能力。总的来说，本研究提出了一种实用的 BP 神经网络模型来预测热力学粘附能，极大地增强了对 Anammox MBR 中粘附性污染行为的预测工具。
