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

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 (R²) of 0.88 and root mean square error (RMSE) of 4.27 for the effluent ammonia nitrogen (NH₄⁺-N_out), and an R² of 0.91 and RMSE of 4.35 for the effluent total nitrogen (TN_out). 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.