Machine Learning (ML) Applications in Water Treatment: Possibilities and Advantages

Artificial intelligence (AI), especially machine learning (ML) algorithms, has gained traction in water treatment processes (WTPs) for tasks such as process optimization, operational decision-making, and cost efficiency. Since 1997, at least 91 peer-reviewed studies have documented the use of AI in various WTP operations, including coagulation/flocculation (41 studies), membrane filtration (21), formation of disinfection byproducts (DBPs) (13), adsorption (16), and other aspects of plant management. This paper critically reviews these studies to evaluate how AI technologies have been applied in WTPs, highlighting both advancements and current limitations. AI has contributed significantly to improving the accuracy of predictions related to coagulant dosage, membrane performance (flux, fouling, and rejection), DBP formation, and contaminant removal. Notably, deep learning (DL) approaches have demonstrated strong feature extraction and data mining capabilities. These have enabled the development of image-based DL models capable of correlating floc morphology with coagulant dosages. Moreover, hybrid models—integrating AI with traditional regression or physical/kinetic approaches—have shown enhanced predictive capabilities. The review also identifies key future research directions aimed at further refining AI-based control systems for water treatment processes.