AI-DRIVEN PREDICTIVE MAINTENANCE FOR WATER TREATMENT: ENHANCING EFFICIENCY AND REDUCING COSTS

Water treatment facilities are essential to public health and environmental protection, yet they face persistent challenges such as equipment degradation, unplanned downtime, and escalating maintenance costs. This systematic review examines the application of artificial intelligence-driven predictive maintenance (AI‑PdM) in water treatment operations, with particular emphasis on its impact on operational efficiency, downtime reduction, and performance improvement. Traditional reactive and preventive maintenance strategies often fall short in optimizing reliability and resource utilization. AI‑PdM addresses these limitations by leveraging real‑time monitoring, advanced analytics, and intelligent fault classification to anticipate failures before they occur, enabling targeted, condition‑based interventions. While existing research highlights the potential of AI‑PdM to improve reliability and reduce costs in industrial contexts, there remains a lack of focused investigation into its application in the water treatment sector, including long‑term performance impacts, integration strategies with legacy systems, and standardized implementation frameworks. Drawing on a comprehensive review of academic research, this study evaluates the operational and economic benefits of AI‑PdM, including reduced maintenance expenditures, improved spare‑parts management, and enhanced process stability. Findings indicate that AI‑PdM offers significant advantages over conventional approaches; however, its successful implementation requires addressing critical barriers such as data quality limitations, integration with legacy systems, and workforce capability gaps. This review positions AI‑PdM as a transformative enabler of sustainable, reliable, and cost‑efficient water treatment operations while identifying priority areas for future research and practice.