article · Results in Engineering
This literature review explores the application of machine learning (ML) in predicting the performance of solar stills, which are cost-effective and low-energy desalination devices. Traditional experimental methods for enhancing solar still designs are time-consuming and expensive. ML models can overcome these issues by establishing relationships between input and output data. The review details frequently used ML methods, covering their principles, benefits, limitations, mathematical descriptions, and evaluation criteria. It also categorises solar still models by design and compares previous studies, explaining discrepancies between ML predictions and experimental results. The study highlights highly accurate models with minimal prediction errors.
As water scarcity increases, efficient desalination methods are vital. Solar stills provide a sustainable solution, and integrating machine learning can accelerate their design and optimisation. This can lead to more effective and accessible water purification technologies, benefiting communities reliant on alternative water sources.
This review supports the development of predictive tools for solar still design and optimisation. Engineers and researchers in water technology could use these insights to select appropriate machine learning models, potentially accelerating the creation of more efficient and cost-effective desalination units. This work is foundational, guiding future applied research and development in solar water purification.
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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 the given dataset. In light of these, this study carries out a literature review that considers the applications of artificial intelligence in solar stills’ performance prediction. The study covers the most repeated machine learning methods employed for performance prediction, focusing on principles, advantages, limitations, and the mathematical description of each method besides model evaluation criteria. Then, a comprehensive analysis is performed on the solar stills models by classifying them according to the design. The work compares the previous studies within a comprehensive analysis that gives 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.
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DOI: 10.1016/j.rineng.2024.101800
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