Accurate prediction and optimization of effluent quality are essential for the stable operation of wastewater treatment plants under increasing influent variability and stringent discharge regulations. This study presents an integrated data-driven framework that combines machine learning, deep learning, model interpretability, and optimization to enhance the performance of a full-scale Modified Ludzack–Ettinger (MLE) process. Three years of operational data from a municipal wastewater treatment plant were used to develop and compare random forest (RF), k-nearest neighbors (K-NN), multilayer perceptron (MLP), and deep neural network (DNN) models for the simultaneous prediction of effluent total organic carbon (TOC), biochemical oxygen demand (BOD), and total nitrogen (TN). Model performance was evaluated using the coefficient of determination (R2) and root mean square error (RMSE), and generalization capability was validated using independent field data. The results show that deep learning models, particularly DNN, outperform conventional machine learning approaches by effectively capturing complex nonlinear and multivariate process dynamics. To improve model interpretability, SHapley Additive exPlanations (SHAP) were applied to identify key operational variables affecting effluent quality. In addition, particle swarm optimization (PSO) was integrated with the trained models to determine optimal operating conditions that minimize effluent pollutant concentrations without requiring structural modifications. Overall, the proposed framework provides an interpretable and practical decision-support tool for proactive wastewater treatment plant operation, contributing to improved operational efficiency and environmental sustainability.
Guo et al. (2026) studied this question.