Application of machine learning modeling in prediction of solar still performance: A comprehensive survey

• Published in: Results in engineering Vol. 21; no. C; p. 101800
• Main Authors: Abdullah, A.S., Joseph, Abanob, Kandeal, A.W., Alawee, Wissam H., Peng, Guilong, Thakur, Amrit Kumar, Sharshir, Swellam W.
• Format: Journal Article
• Language: English
• Published: United States Elsevier B.V 01.03.2024; Elsevier
• Subjects: Machine learning; Modeling; Optimization; Performance prediction; Solar still; Performance prediction; Solar still; Modeling; Optimization; Machine learning
• ISSN: 2590-1230; 2590-1230
• DOI: 10.1016/j.rineng.2024.101800

Being a cheap, simple, and low-energy consumer, solar stills have been introduced by water and energy scientists as an alternative desalination method to fossil fuel-based ones. A wide variety of designs and modifications have been applied to enhance the solar stills' performance, which may be associated with experimental works that require time and cost. Therefore, coupling solar stills with state-of-the-art machine learning is expected to overcome these disadvantages of experimental work. Artificial intelligence models try to build relationships between the input and output data similar to the human brains depending on given dataset. In light of these, this paper carries out a literature review that considers the applications of artificial intelligence in solar stills’ performance prediction. It covers the most repeated machine learning methods employed for performance prediction, focusing on principles, advantages, limitations, and the mathematical description of each method; besides the models' evaluation criteria. Then, comprehensive discussions are performed on the solar stills models by classifying them according to the design. The work compares the previous studies within comprehensive analyses that give reasons for the authors' findings, highlighting the reasons for the variation between the models' prediction and experimental findings. Accordingly, models with root mean square errors close to zero are highlighted throughout the review. •A comprehensive review of machine learning-based solar stills is presented.•The bibliometric analysis of the topic is conducted.•Principles and the mathematical description of common models is given.•Various investigated solar still designs and machine learning significance are illustrated and compared.•Advantages and limitations of the models are highlighted.