Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities

Area of Science:

• Environmental Engineering
• Water Treatment Technologies
• Artificial Intelligence in Environmental Science

Background:

• Artificial intelligence (AI) and machine learning (ML) are increasingly utilized in water treatment processes (WTPs) for decision-making, process control, optimization, and cost management.
• A review of 91 articles since 1997 reveals AI applications in coagulation/flocculation, membrane filtration, disinfection byproducts (DBPs) formation, adsorption, and operational management within WTPs.

Purpose of the Study:

• To assess the development and applications of AI techniques in WTPs.
• To identify the limitations and areas for improvement in AI applications for water treatment.
• To highlight future research directions for enhanced WTP control.

Main Methods:

• Systematic review of 91 peer-reviewed articles published since 1997 on AI applications in WTPs.
• Analysis of AI techniques applied to specific WTP processes including coagulation/flocculation, membrane filtration, DBPs formation, and adsorption.
• Evaluation of the performance and capabilities of different AI approaches, including deep learning and hybrid techniques.

Main Results:

• AI techniques have improved predictive capabilities for coagulant dosages, membrane flux, rejection, fouling, DBPs formation, and pollutant removal in WTPs.
• Deep learning (DL) demonstrates strong feature extraction and data mining abilities, enabling image recognition frameworks for floc analysis and coagulant dosage prediction.
• Hybrid AI techniques, combining regression or physical/kinetics models with AI, exhibit superior predictive performance.

Conclusions:

• AI, particularly ML and DL, offers significant advancements in optimizing and controlling water treatment processes.
• Future research should focus on refining AI techniques, including hybrid models and DL-based image recognition, for more effective WTP management.
• Continued development of AI applications is crucial for addressing challenges and improving the efficiency and predictive accuracy of WTPs.