The Role of AI in Municipal Wastewater Treatment and Management: A Comprehensive Review
The fast pace of urbanization, tightening discharge requirements, and efficient wastewater treatment require that municipal wastewater treatment plants (WWTPs) move to adaptive and intelligent modes of operation. The present review provides an analysis of the application of artificial intelligence (AI) to different pre‐ and posttreatment steps in the context of municipal WWTPs. Specifically, this review considers how machine learning (ML), deep learning (DL), evolutionary optimization, reinforcement learning, and hybrid AI–mechanistic models can be utilized in municipal wastewater processes. AI‐based approaches are reviewed in the area of activated sludge processes such as dissolved oxygen control, nutrient removal, effluent quality estimation, and energy management; membrane bioreactor (MBR) processes such as membrane fouling and transmembrane pressure prediction; anaerobic digestion (AD) processes for biogas generation, stability, energy recovery, and nutrients recovery; and sludge processes such as dewatering, polymer dosage optimization, digestion, and resource recovery. Moreover, AI methods are critically evaluated in the areas of nitrogen and phosphorus removal, N 2 O emissions prediction, fault detection, predictive maintenance, and plant‐wide optimization. Evidence is provided on the implementation of AI in full‐scale municipal and WWTPs with a shift from prediction and soft sensing to optimization and supervision, including aeration control, carbon dosing, coagulant optimization, and sludge dewatering. Nevertheless, the findings heavily depend on plant layout, data quality, sensor accuracy, AI architecture, and validation scheme, and none of the AI architectures currently exist that can be deemed superior to other architectures across all applications. Digital twins, explainable AI, edge computing, and federated learning represent exciting directions for future integrated and robust WWTP management. In addition, autonomous plant‐level control and AI‐assisted AD‐nutrient recovery have not been sufficiently developed yet. The main issues to solve are cross‐plant validation, uncertainty analysis, cybersecurity, interpretability, seamless integration into existing supervisory control and data acquisition (SCADA)/programmable logic controller (PLC) networks, and long‐term benefits demonstration.