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Utilising Artificial Intelligence to Predict Membrane Behaviour in Water Purification and Desalination
by Reza Shahouni 1ORCID,Mohsen Abbasi 2,*ORCID,Mahdieh Dibaj 3 andMohammad Akrami 3,*ORCID
1
School of Chemical Engineering, Iran University of Science and Technology, Tehran 13114-16846, Iran
2
Department of Chemical Engineering, Faculty of Petroleum, Gas and Petrochemical Engineering, Persian Gulf University, Bushehr 75169-13817, Iran
3
Department of Engineering, University of Exeter, Exeter EX4 4QF, UK
*
Authors to whom correspondence should be addressed.
Water 2024, 16(20), 2940; https://doi.org/10.3390/w16202940
Submission received: 10 September 2024 / Revised: 5 October 2024 / Accepted: 10 October 2024 / Published: 15 October 2024
(This article belongs to the Section Wastewater Treatment and Reuse)
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Abstract
Water scarcity is a critical global issue, necessitating efficient water purification and desalination methods. Membrane separation methods are environmentally friendly and consume less energy, making them more economical compared to other desalination and purification methods. This survey explores the application of artificial intelligence (AI) to predict membrane behaviour in water purification and desalination processes. Various AI platforms, including machine learning (ML) and artificial neural networks (ANNs), were utilised to model water flux, predict fouling behaviour, simulate micropollutant dynamics and optimise operational parameters. Specifically, models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and support vector machines (SVMs) have demonstrated superior predictive capabilities in these applications. This review studies recent advancements, emphasising the superior predictive capabilities of AI models compared to traditional methods. Key findings include the development of AI models for vario