Solar stills: the future enabled by machine learning

Abstract

Desalination is a highly energy-intensive process often requiring the consumption of costly fossil fuels, inevitably causing various environmental hazards. As a sustainable and renewable energy source, solar energy is anticipated to alleviate such environmental concerns associated with the energy-intensive desalination process. 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 their low cost and simple operation. In this review, machine learning techniques are particularly emphasized, along with exploring the differences 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 specifically highlight 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.