Machine learning-driven design of membranes for saline and produced water treatment across scales

Abstract

Membrane-based desalination technologies are critical for addressing global water scarcity; however, their performance optimization remains a complex challenge. This review explores the potential of machine learning (ML) in advancing membrane desalination, with a focus on process optimization, fouling mitigation, and performance prediction. We provide a comprehensive analysis of ML techniques, including artificial neural networks (ANNs), support vector machines (SVMs), and random forest (RF), applied to membrane systems such as reverse osmosis (RO), forward osmosis (FO), and for the treatment of produced water. The study highlights how data-driven models enhance decision-making by correlating operational parameters (e.g., pressure, temperature, feed salinity) with membrane efficiency, energy consumption, and fouling behavior. A key emphasis is placed on ANN-based frameworks for real-time monitoring and predictive control, demonstrating their superiority in modeling non-linear interactions compared to traditional mechanistic approaches. We also examine ML applications in optimizing design parameters, maintenance strategies, and renewable-energy-integrated desalination systems. Challenges such as data scarcity and model generalizability are discussed, alongside future directions for integrating ML with emerging membrane materials and hybrid processes. This review highlights ML's role in bridging theoretical and practical gaps in desalination, offering actionable insights for researchers and industry experts to deploy intelligent and sustainable water treatment solutions.