Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review

Abstract

Because of its robust autonomous learning and ability to address complex problems, artificial intelligence (AI) has increasingly demonstrated its potential to solve the challenges faced in drinking water treatment (DWT). AI technology provides technical support for the management and operation of DWT processes, which is more efficient than relying solely on human operations. AI-based data analysis and evolutionary learning mechanisms are capable of realizing water quality diagnosis, autonomous decision making and operation process optimization and have the potential to establish a universal process analysis and predictive model platform. This review briefly introduces AI technologies that are widely used in DWT. Moreover, this paper reviews in detail the mature applications and latest discoveries of AI and machine learning technologies in the fields of source water quality, coagulation/flocculation, disinfection and membrane filtration, including source water contaminant monitoring and identification, accurate and efficient prediction of coagulation dosage, analysis of the formation of disinfection by-products and advanced control of membrane fouling. Finally, the challenges facing AI technologies and the issues that need further study are discussed; these challenges can be briefly summarized as a) obtaining more effective characterization data to screen and identify targeted contaminants in the complex background with the assistance of AI technologies and b) establishing a macro intelligence model and decision scheme for entire drinking water treatment plants (DWTPs) to support the management of the water supply system.