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

Journal: Bioresource technology
PMID: 38901751

Abstract

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.

Authors

  • Xiang Cai
    School of Business, Guilin University of Electronic Technology, Guilin 541004, China.
  • Si Pang
    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.
  • Meijia Zhang
    College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China.
  • Jiaheng Teng
    College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China.
  • Hongjun Lin
    College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China.
  • Siqing Xia
    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. Electronic address: siqingxia@tongji.edu.cn.

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