A comprehensive review on forward osmosis mass transfer and fouling: Mathematical modeling, mechanism, prediction and optimization - Goi, Y. K.; Li, M.; Liang, Y. Y. (Elsevier, 2025)
This review examines the modeling of concentration polarization (CP) and fouling in forward osmosis (FO) systems, exploring recent advancements and ongoing challenges in predictive modeling and system optimization. Analytical models have improved the prediction of water flux and solute rejection in FO systems, but they often overestimate flux and struggle to predict time-dependent fouling. Modified models and model-fitting approaches have been developed to address these limitations, though recalibration is necessary under changing operating conditions. Critical parameters such as cross-flow velocity, water permeability and membrane dimensions have a significant impact on FO performance, therefore requiring careful optimization. Fouling remains a major concern in FO processes due to cake-enhanced osmotic pressure (CEOP). Models such as cake filtration and modified layered transport have been developed to better understand fouling mechanisms. Machine learning (ML) holds promise in overcoming the limitations of traditional models by enabling more accurate simulations of complex FO systems and optimizing system design. Coupling FO with other desalination technologies, such as RO, MED, MSF and MD offer significant benefits, including higher water recovery, reduced fouling and improved energy efficiency. However, modeling fouling in scaled-up FO processes remains challenging due to complex flow patterns, prompting the proposal of a simplified 1D finite difference method to efficiently capture fouling impacts.