Multi-pollutant prediction and process parameter optimization of a wastewater treatment plant based on machine learning models

Conventional wastewater treatment models, heavily reliant on manual expertise and offline monitoring, cause response delays, struggle with inefficient fluctuations, and lead to high resource consumption. To overcome these challenges, this study established a data-driven multi-pollutant prediction model using three years of daily monitoring data from a wastewater treatment plant (WWTP). The model integrates data cleaning, advanced feature engineering, multi-dimensional intelligent feature selection, and an ensemble learning strategy. Furthermore, combined with nitrification/denitrification mechanisms, a back-calculation model employing Particle Swarm Optimization-Support Vector Regression (PSO-SVR) was developed to predict optimal aeration intensity and carbon source dosage. The prediction model excelled, achieving R 2 values of 0.96 for total nitrogen (TN), 0.94 for total phosphorus (TP), 0.91 for ammonia nitrogen (NH 3 -N), 0.92 for influent wastewater volume (Q w ), and 0.75 for chemical oxygen demand (COD). The back-calculation models also demonstrated high precision, with test set R 2 of 0.94 for aeration rate and 0.96 for carbon dosage. Additionally, this strategy achieved an estimated 15–20% aeration energy savings and reduced carbon source overdosing to below 5%, while ensuring stable effluent compliance. This closed-loop approach of “pollutant concentration prediction → process parameter back-calculation” dynamically responds to fluctuations, enabling quantitative and refined WWTP management, thereby demonstrating significant practical impact for improving treatment efficiency while reducing energy and resource consumption.