Publication Details
Abstract
Reverse osmosis (RO) remains the dominant desalination technology, offering relatively low energy consumption and high water quality. Despite its widespread deployment, challenges such as fouling, energy intensity, and limited predictive control hinder its long-term sustainability. This review critically examines advances in RO system modeling, the influence of operating conditions, and the role of artificial neural networks (ANNs) and cleaning strategies. We compare mechanistic, empirical, and hybrid modeling approaches, evaluating their predictive capacity and limitations. ANN-based methods are assessed for their ability to capture nonlinearities and enhance operational optimization, but their dependence on large datasets and poor interpretability remain unresolved challenges. Cleaning strategies, ranging from conventional chemical treatments to novel physical and forward-osmosis-based approaches, are analyzed for effectiveness, costs, and environmental impact. By synthesizing strengths, weaknesses, and knowledge gaps across these domains, this review highlights future directions, including the integration of physics-informed machine learning, real-time monitoring for predictive fouling control, and sustainable, non-chemical cleaning innovations.