AI-Driven Optimization of Wastewater Treatment Processes for Sustainable Environmental Management

Abstract

Wastewater treatment plants are subjected to continuously varying hydraulic and pollutant loads, while treatment performance, energy consumption and environmental impact must be maintained. This study presents an artificial-intelligence-based framework consisting of a predictive machine learning procedure and a constrained multi-objective optimisation for the adaptive operation of activated-sludge wastewater treatment. A BSM1-inspired synthetic benchmark comprising 16,128 operating observations was generated that encompass variations in influent flow, chemical oxygen demand (COD), total nitrogen (TN), ammonium nitrogen (NH4-N), temperature, dissolved oxygen (DO) set-point, internal recycle ratio, sludge retention time (SRT) and return activated sludge ratio. Multiple linear regression, random forest, Extra Trees and XGBoost models were assessed for the prediction of specific energy demand and effluent-quality indicators. XGBoost provided overall best predictive performance with an R² of 0.995 for energy demand, 0.948 for effluent COD, 0.970 for TN, 0.987 for NH4-N and 0.981 for the combined effluent-quality index (EQI). The trained XGBoost surrogate was coupled to a constrained particle swarm optimisation routine that identified operating set-points resulting in lower energy consumption without compromising effluent quality. When applied to 120 unseen influent conditions, the proposed framework reduced simulated specific energy demand from 0.462 to 0.358 kWh m−3 (22.5%) while decreasing COD, TN, NH4-N and the relative EQI by 7.3, 8.3, 2.8 and 9.7%, respectively. DO set-point and SRT were identified as the major controllable variables. The results demonstrated the effectiveness of integrating predictive modeling with prescriptive optimization to achieve energy-efficient and environmentally sustainable wastewater treatment. The study was conducted as a simulation-based proof of concept and requires validation on a long-term plant data basis prior to implementation.