Unsteady-state dynamics and AI in membrane desalination: challenges and emerging opportunities

Abstract

Membrane desalination is a pivotal technology for addressing global water scarcity, yet most studies focus on steady-state operation. Unsteady and cyclic processes remain comparatively underexplored, although they are central to advancing recovery, fouling resistance, and operational flexibility. This review synthesizes over 100 publications (2015–2025) covering reverse osmosis (RO) configurations such as closed-circuit RO (CCRO), batch RO (BRO), flow-reversal RO (FRRO) and emerging hybrids, as well as flow in MD, FO, ED and PRO. Quantitative comparisons reveal that CCRO can reach up to 98 % recovery but suffers from flushing inefficiency with ∼11 % residual brine per cycle; BRO reduces energy consumption by ∼30 % under brackish water treatment at 95 % recovery; and FRRO retrofits have lowered specific energy consumption by ∼14 % while enabling recoveries of 90–91 %. Beyond mechanistic modeling, the review highlights the integration of computational fluid dynamics (CFD) and machine learning (ML), including explainable AI (e.g., SHAP), reinforcement learning, and physics-informed neural operators, which have demonstrated up to 16 % operating cost reduction and > 100 % membrane life extension in industrial-scale RO. We identify three critical gaps: (i) flushing inefficiency and cycle-to-cycle salt accumulation, (ii) limited pilot-scale and long-term datasets for unsteady operations, and (iii) challenges in integrating CFD with AI frameworks. By bridging mechanistic and data-driven approaches, this review outlines opportunities to develop digital twin frameworks for resilient, efficient, and intelligent unsteady desalinatio