The growing energy demand is increasing with the increase in population in all the developing countries. Solar energy being sustainable energy, is used as a predominant source in producing energy that is useful in day to day life, particularly in generating pure water through the solar desalination process. Simultaneously, a greater extent of adhering to Machine learning techniques in the applications of energy, fluid, manufacturing fields is a boom nowadays.&nbsp; Machine learning forecasting attributed a quality and quantity improvement in the performance of energy-based projects in less time.The present study focuses on a detailed review of how a solar distillation system performance evaluation can be made with ongoing higher-end deep learning techniques explicitly useful in optimising and evaluating the design and energy parameters. Solar stills have the advantage of using solar radiation as they are the simple thermal energy source for seawater and industrial water desalination. The Solar distillation system is viable to the climatic changes concerning experimentation time as a critical factor.In the last few decades, researchers worldwide have attempted to investigate and optimize the various factors for maximizing efficiency and distillate output. From the existing literature, it has been found that basin water depth is an important factor affecting the distillate water output of the still. The basin water depth is found to be inversely proportional to the productivity of the still. This is because keeping the lower depth of water in the basin results in lower thermal storage and, therefore, better evaporation heat transfer coefficient and, hence, higher productivity. Therefore, exact forecasting is essential in evaluating for the enhanced techno-economic analysis. The current study's novelty is to find the effective use of Machine learning techniques and examine how an in-depth learning approach makes a resilient influence on energy-based system performance prediction. This paper creates ease in performing research via machine learning techniques profoundly on deep learning techniques in solar distillation methods. Further, this process enables the distillation systems to become smart systems while working on deep learning techniques, which is getting more attention than the traditional statistical approach.To draw the feasibility of&nbsp;adopting the suitable supervised or unsupervised Machine learning methods, a complete study on the application of ML models is made accordingly. A comparative analysis of deep learning models applied in the advancement of the solar distillation process is explained in this study.Keywords: Machine learning, solar still, optimization, Gaussian model, Spearman correlation, RNN, CNN, RF, MATLAB

Solar Distillation Systems Enriched With Machine Learning Techniques: A Review

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

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