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The incorporation of Artificial Intelligence (AI) techniques into membrane-based systems has transformed their design, optimization, and management in various fields, notably, Reverse Osmosis (RO) and Ultrafiltration (UF). This comprehensive review evaluates the strategic application of AI methodologies across different membrane separation processes, furnishing practical insights for methodology selection and refinement. Ranging from predictive Machine Learning (ML) models for microfiltration to sophisticated neural networks for fouling alleviation in ultrafiltration, this review elucidates the alignment of AI approaches with specific application requisites. Despite persistent challenges such as data scarcity, interpretability of models, and computational demands, promising frontiers are emerging, including the amalgamation of AI with sensor technologies for real-time monitoring and control, and the utilization of generative adversarial networks for membrane material innovation. As the domain of AI in membrane separation progresses, interdisciplinary collaboration becomes paramount in surmounting existing obstacles and exploiting nascent prospects. This review underscores the necessity of comprehending AI methodologies tailored to diverse membrane separation context, steering future investigations towards heightened efficacy, sustainability, and ingenuity. • AI can enhance performance and sustainability in membrane separation processes. • ML models predict permeate flux, rejection rates, and fouling with high accuracy. • AI optimization reduces energy use and fouling in membrane separation processes. • Hybrid models improve the accuracy and interpretability of AI predictions. • Explainable AI and new applications drive future membrane technology advancements.
Jafari et al. (Mon,) studied this question.