Enhanced nitrogen prediction and mechanistic process analysis in high-salinity wastewater treatment using interpretable machine learning approach.

Journal: Bioresource technology
PMID: 40081773

Abstract

This study introduces an interpretable machine learning framework to predict nitrogen removal in membrane bioreactor (MBR) treating high-salinity wastewater. By integrating Shapley additive explanations (SHAP) with Categorical Boosting (CatBoost), we address the critical gap in linking predictive accuracy to operational decision-making for saline systems. CatBoost achieved the best performance, with an coefficient of determination (R) of 0.88 and root mean square error (RMSE) of 4.27 for the effluent ammonia nitrogen (NH-N), and an R of 0.91 and RMSE of 4.35 for the effluent total nitrogen (TN). SHAP analysis uniquely revealed salinity's dual role in inhibiting nitrifying enzymes and disrupting carbon metabolism, with dissolved oxygen, pH and chemical oxygen demand removal efficiency as key regulators. Temperature and carbon-to-nitrogen ratio further modulated total nitrogen dynamics through electron donor availability and microbial activity. The proposed SHAP-CatBoost model in high salinity MBR combines predictive modelling with mechanical process control.

Authors

  • Qing Wei
    Department of Computer Science and Department of Biological Science, Purdue University, West Lafayette, IN 47907, USA.
  • Zuxin Xu
    State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Yangpu District, Shanghai 200092, China; Ministry of Education Key Laboratory of Yangtze River Water Environment, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China.
  • Hailong Yin
    Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China; Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai 200092, China. Electronic address: yinhailong@tongji.edu.cn.

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