Applying machine learning and AI for nanofiltration membranes water applications: a review

Area of Science:

• Water treatment technologies
• Membrane science
• Materials science

Background:

• Nanofiltration (NF) is crucial for water treatment, desalination, and wastewater management due to its high solute rejection and flux.
• Current NF applications face limitations in scalability and reliability due to complex fouling dynamics and the flux-selectivity trade-off.
• Traditional models struggle to predict NF performance in real-world water matrices, creating a gap between predictive accuracy and operational reality.

Purpose of the Study:

• To comprehensively review the integration of machine learning (ML) and artificial intelligence (AI) in nanofiltration (NF) research.
• To assess ML/AI applications in NF membrane fabrication optimization, performance prediction, and fouling diagnosis/mitigation.
• To identify key challenges and future directions for ML/AI in NF technology.

Main Methods:

• Systematic literature review of 100 NF-ML publications from the past decade.
• Analysis of ML/AI applications across fabrication, performance prediction, and fouling management domains.
• Identification of methodological challenges and future research trends.

Main Results:

• ML and AI show significant promise in optimizing NF membrane fabrication processes.
• AI/ML models can accurately predict NF membrane performance and identify fouling issues.
• The review highlights data scarcity, heterogeneity, and validation as key challenges in current NF-ML research.

Conclusions:

• Integrating ML/AI into NF processes offers substantial improvements in efficiency and reliability for water treatment.
• Future advancements require addressing data limitations and developing hybrid ML-mechanistic models.
• Reinforcement learning holds potential for real-time adaptive control of NF systems.