Predictive Modeling of Brackish Surface Water Quality for Reverse Osmosis Desalination Plants Using Advanced Machine Learning Techniques

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已出版: 12月 17, 2025

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Cherki Lahlou
Laboratory of Industrial Engineering, Sciences and Technology Faculty, Sultan Moulay Slimane University, Beni Mellal 23000, Morocco
Belaid Bouikhalene
Laboratory of Mathematics Innovation and Information Technology (LIMATI), Polydisciplinary Faculty Beni Mellal, Sultan Moulay Slimane University, Beni Mellal 23000, Morocco
Jamaa Bengourram
Laboratory of Mathematics Innovation and Information Technology (LIMATI), Polydisciplinary Faculty Beni Mellal, Sultan Moulay Slimane University, Beni Mellal 23000, Morocco
Hassan Latrache
Laboratory of Industrial Engineering, Sciences and Technology Faculty, Sultan Moulay Slimane University, Beni Mellal 23000, Morocco
Radouane El Amri
Environmental, Ecological and Agro-Industrial Engineering Laboratory, Department of Chemistry and Environment, Faculty of Sciences and Technology, University Sultan Moulay Slimane, Beni Mellal 23000, Morocco

摘要

Artificial intelligence (AI) has proven highly effective in optimizing water treatment processes, particularly for monitoring reverse osmosis (RO) desalination plants treating brackish surface water. These systems face complex, non-linear variations in feed water quality, making real-time monitoring crucial to avoid performance loss and membrane fouling. This study presents a machine learning (ML)–based framework to predict the water quality index (WQI) and enable rapid decision-making by classifying water quality (WQC) into four actionable categories: Excellent, Good, Poor, and Unsuitable. Using 11 key water quality parameters, the model provides an efficient and reliable approach for prediction and classification. Among tested algorithms, the multi-layer perceptron (MLP) achieved the best WQI prediction performance, with an R2 of 98.19%, a mean absolute error (MAE) of 0.0182, and a mean squared error (MSE) of 0.0043. For WQC, the XGBoost algorithm outperformed others, reaching 99.84% accuracy. The results demonstrate the strong potential of ML techniques to enhance water quality monitoring and management in RO desalination plants, supporting efficient operation and timely intervention.

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Cherki Lahlou, Belaid Bouikhalene, Jamaa Bengourram, Hassan Latrache, & Radouane El Amri. (2025). Predictive Modeling of Brackish Surface Water Quality for Reverse Osmosis Desalination Plants Using Advanced Machine Learning Techniques. Science & Technology Asia, 30(4), 201–216. 取读于 从 https://ph02.tci-thaijo.org/index.php/SciTechAsia/article/view/258148
期
Vol.30 No.4 (October-December 2025)
栏目
Engineering
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