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Comparative Assessment of Artificial Intelligence Models for Predicting Water Quality in a Reverse Osmosis
Cherifa Mezhoud1, Rachid Sahnoune2, Faiza Bouchraki1
1Université de Bejaia, Faculté de Technologie, Département d'Hydraulique, Laboratoire de Recherche en Hydraulique Appliquée et Environnement (LRHAE), Route de Targa-Ouzemmour, Bejaia, Algeria.
Summary
Artificial intelligence (AI) enhances seawater desalination by predicting water quality. Machine learning models like XGBoost and linear regression offer robust solutions for optimizing reverse osmosis systems, addressing challenges like raw water variability and energy consumption.
Area of Science:
- Environmental Engineering
- Water Treatment Technologies
- Artificial Intelligence in Engineering
Background:
- Freshwater scarcity necessitates efficient seawater desalination.
- Reverse osmosis (RO) systems face challenges including raw water variability, high energy use, and membrane degradation.
- Predictive monitoring is crucial for optimizing desalination plant operations.
Purpose of the Study:
- To investigate the application of artificial intelligence (AI) for predictive monitoring of a seawater desalination plant.
- To evaluate the performance of various supervised learning algorithms in predicting water quality parameters.
- To identify optimal AI models for enhancing the efficiency and sustainability of desalination processes.
Main Methods:
- Utilized real operational data from the Cap Djinet desalination plant (Algeria).
- Evaluated six supervised learning algorithms: linear regression (LR), polynomial regression (PR), support vector regression (SVR), random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP).
- Assessed model performance using metrics such as R-squared (R²), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE).
Main Results:
- Ensemble models, particularly XGBoost (R² = 0.999, MAPE = 2.23%), demonstrated superior performance.
- Random Forest (RF) and Support Vector Regression (SVR) also showed high accuracy.
- Linear Regression (LR) proved robust (R² = 0.999, RMSE = 4.90) and suitable for daily operations.
- Complex models like Multilayer Perceptron (MLP) were sensitive to limited data (MAPE = 65.45%).
Conclusions:
- XGBoost, RF, SVR, and LR show significant potential for predictive monitoring in desalination.
- AI-driven predictive monitoring can aid in the sustainable optimization of reverse osmosis processes.
- Model selection should consider data availability, with simpler models like LR being effective in certain contexts.
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