Solar Stills: The Future Enable by Machine Learning

Abstract

The desalination process is known to be highly energy-intensive. As a sustainable and renewable energy source alternative, solar energy is anticipated to alleviate the environmental hazards of relying on costly fossil fuels for water treatment procedures. Recently, machine learning, a powerful data analysis method, has been employed for modeling and prediction to enhance the productivity of solar stills, an effective solution to water scarcity owing to low cost and simple operation. In this review, machine learning techniques are particularly emphasized, along with exploring distinctions between solar stills and other solar desalination technologies. Machine learning models can achieve further optimization through additional avenues such as model selection, hyperparameter tuning, feature selection, and dataset management. The findings position the crucial role of machine learning in enhancing solar desalination through improved prediction and optimization. Furthermore, this paper discussed different machine-learning prediction techniques while offering suggestions for future research in the field.