Predicting thermodynamic adhesion energies of membrane fouling planktonic anammox MBR via backpropagation neural network model
文献类型: 外文期刊
作者: Cai, Xiang 1 ; Pang, Si 2 ; Zhang, Meijia 1 ; Teng, Jiaheng 1 ; Lin, Hongjun 1 ; Xia, Siqing 2 ;
作者机构: 1.Zhejiang Normal Univ, Coll Geog & Environm Sci, Jinhua 321004, Peoples R China
2.Tongji Univ, Coll Environm Sci & Engn, State Key Lab Pollut Control & Resource Reuse, Shanghai 200092, Peoples R China
3.Shanghai Inst Pollut Control & Ecol Secur, Shanghai 200092, Peoples R China
4.Shanghai Acad Agr Sci, Inst Ecoenvironm & Plant Protect, Shanghai 201403, Peoples R China
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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