Membrane Fouling Prediction and Control Using AI and Machine Learning: A Comprehensive Review

Membrane fouling is a persistent challenge in membrane-based technologies, significantly impacting efficiency, operational costs, and system lifespan in applications like water treatment, desalination, and industrial processing. Fouling, caused by the accumulation of particulates, organic compounds, and microorganisms, leads to reduced permeability, increased energy demands, and frequent maintenance. Traditional fouling control approaches, relying on empirical models and reactive strategies, often fail to address these issues efficiently. In this context, artificial intelligence (AI) and machine learning (ML) have emerged as innovative tools offering predictive and proactive solutions for fouling management. By utilizing historical and real-time data, AI/ML techniques such as artificial neural networks, support vector machines, and ensemble models enable accurate prediction of fouling onset, identification of fouling mechanisms, and optimization of control measures. This review provides a detailed examination of the integration of AI/ML in membrane fouling prediction and mitigation, discussing advanced algorithms, the role of sensor-based monitoring, and the importance of robust datasets in enhancing predictive accuracy. Case studies highlighting successful AI/ML applications across various membrane processes are presented, demonstrating their transformative potential in improving system performance. Emerging trends, such as hybrid modeling and IoT-enabled smart systems, are explored, alongside a critical analysis of research gaps and opportunities. This review emphasizes AI/ML as a cornerstone for sustainable, cost-effective membrane operations.