Integrating predictive and hybrid Machine Learning approaches for optimizing solar still performance: A comprehensive review

Abstract

The increasing global need for freshwater, coupled with the imperative for sustainable and energy-efficient solutions, has fueled interest in solar distillation technologies. Solar stills (SSs) offer a simple, low-cost, and environmentally friendly approach to desalination. However, their performance can be significantly influenced by various factors, including climatic conditions, design parameters, and operational variables. To address these challenges and predict SS performance, machine learning (ML) techniques have emerged as a powerful tool. This review explores the application of various ML models, including Support Vector Machines (SVM), Multi-Layer Perceptrons (MLP), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Decision Trees (DT), and hybrid ML/metaheuristic optimizer models, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), in predicting water production rates, managing energy consumption, and providing decision support for operators. The review highlights the potential of these models to enhance the efficiency and sustainability of solar desalination systems. By leveraging data-driven insights and predictive modeling, ML-based approaches enable the prediction of performance metrics, identification of optimal operating conditions, and real-time monitoring and control. Furthermore, hybrid ML/metaheuristic models, which combine algorithms like SVM, MLP, and ANFIS with optimization techniques, offer enhanced reliability and resilience in complex scenarios. This review emphasizes the significant potential of ML in advancing solar distillation technologies, showing that integrating ML techniques into SS systems can lead to more efficient, sustainable, and cost-effective solutions to address global water scarcity challenges.