Performance Evaluation of Domestic Wastewater Treatment Using SBR With Air Recirculation and Optimize Its Performance...

Conventional sequencing batch reactors (SBRs) suffer from poor oxygen utilization efficiency, typically consuming only 1%-2% of supplied oxygen while wasting 18%-20% in the exhaust gas. Although conventional intermittent aeration can lower energy costs, it frequently leads to rapid dissolved oxygen (DO) depletion during blower-off periods, causing biological process failure. To address these critical limitations, this study presents a unique scientific contribution through the synergistic integration of a physical air-recirculation system with a predictive machine learning (ML) optimization framework. A laboratory-scale SBR equipped with a pressurized tank was designed to capture and reuse aeration off-gas, improving oxygen utilization and reducing blower operating time. Continuous aeration achieved optimal performance at 4 h, with COD and NH₄ ⁺ removal efficiencies of 72.1% and 91.7%, respectively, but energy demand increased proportionally with aeration duration. In contrast, intermittent aeration with recirculation maintained comparable pollutant removal while reducing energy consumption by 30%-40%. The 5 min on/5 min off regime provided the best balance, sustaining dissolved oxygen levels and stable biological activity. Machine learning models further enhanced optimization: Support vector regression (SVR) accurately predicted energy use, while XGBoost captured nonlinear pollutant removal dynamics. Model-based response surfaces identified moderate intermittent aeration (4- to 6-min cycles, 4-h duration) as the most favorable operating region. This integrated approach offers strong potential for sustainable decentralized wastewater treatment.