Using Artificial Intelligence to Improve the Operational Efficiency of Water Treatment Plants

Abstract

Water treatment plants face increasing challenges, including fluctuating raw water quality, high energy consumption, stricter regulatory requirements, and the need to reduce carbon emissions. Artificial Intelligence (AI) and Machine Learning (ML) offer promising solutions to improve the operational efficiency of these plants through real-time monitoring, predictive maintenance, adaptive control, and multi-objective optimization. This paper reviews recent applications of AI in water treatment plants, including water quality prediction, chemical dosing optimization, energy savings, predictive maintenance, and Digital Twin modeling. It also discusses the main challenges hindering the adoption of these technologies, such as data quality, model interpretability, and generalizability across different plants, while providing insights into future trends in this field.