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2025-06-12
Membrane Fouling Prediction and Control Using AI and Machine Learning: A Comprehensive Review
Authors
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Doaa Salim Musallam Samhan Al-Kathiri
Chemical Engineering, College of Engineering and Technology, University of Technology and Applied Sciences-Salalah, Salalah 211, Sultanate of Oman
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Gaddala Babu Rao
Department of Chemical Engineering, School of Studies of Engineering and Technology, Guru Ghasidas Vishwavidyalaya (A Central University), Koni, Bilaspur, Chhattisgarh 495009, India
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Noor Mohammed Said Qahoor
Chemical Engineering, College of Engineering and Technology, University of Technology and Applied Sciences-Salalah, Salalah 211, Sultanate of Oman
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Saikat Banerjee
Chemical Engineering, College of Engineering and Technology, University of Technology and Applied Sciences-Salalah, Salalah 211, Sultanate of Oman
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Naladi Ram Babu
Department of Electrical and Electronics Engineering, Aditya University, Surampalem, East Godavari, Andhra Pradesh 533437, India
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Gadidamalla Kavitha
Department of Chemical Engineering, RVR & JC College of Engineering (A), Chowdavaram, Guntur, A.P 522019, India
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Nageswara Rao Lakkimsetty
Department of Chemical and Petroleum Engineering, American University of Ras Al Khaimah (AURAK), Ras al Khaimah 72603, United Arab Emirate
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Rakesh Namdeti
Chemical Engineering, College of Engineering and Technology, University of Technology and Applied Sciences-Salalah, Salalah 211, Sultanate of Oman
DOI:
https://doi.org/10.30564/jees.v7i6.8630Abstract
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.
Keywords:
Membrane Fouling; Artificial Intelligence (AI); Machine Learning (ML); Fouling Prediction; Smart Membrane SystemsReferences
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Doaa Salim Musallam Samhan Al-Kathiri, Gaddala Babu Rao, Noor Mohammed Said Qahoor, Saikat Banerjee, Naladi Ram Babu, Gadidamalla Kavitha, Nageswara Rao Lakkimsetty, & Namdeti, R. (2025). Membrane Fouling Prediction and Control Using AI and Machine Learning: A Comprehensive Review. Journal of Environmental & Earth Sciences, 7(6), 315–350. https://doi.org/10.30564/jees.v7i6.8630
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Copyright © 2025 Doaa Salim Musallam Samhan Al-Kathiri, Gaddala Babu Rao, Noor Mohammed Said Qahoor, Saikat Banerjee, Naladi Ram Babu, Gadidamalla Kavitha, Nageswara Rao Lakkimsetty, Rakesh Namdeti
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