Fuzzy control system

Engineering

Yield (engineering)

Solar still

Materials science

Support vector machine

Exergy efficiency

Adaptive neuro fuzzy inference system

Agricultural engineering

Biochemistry

Computer science

Membrane

Desalination

Fuzzy logic

Process engineering

Environmental science

Artificial intelligence

Composite material

Chemistry

Artificial neural network

Machine learning

Exergy

Sustainable Energy Technologies and Assessments

This paper aims to enhance the performance of conventional solar still (CSS) using a low cost heat localization bilayered structure (HLBS). The HLBS consists of a bottom supporting layer (SL) made of low thermal conductivity as well as low density material and a top absorbing layer (AL) made of a photo thermal material with a high sunlight absorptivity as well as an enhanced conversion efficiency. The developed HLBS helps in increasing the evaporation rate and minimize the heat losses in a modified solar still (MSS). Two similar SSs were designed and tested to evaluate SSs' performance without and with HLBS (CSS and MSS). Moreover, three machine learning (ML) methods were utilized as predictive tools to obtain the water yield of the SSs, namely artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). The prediction accuracy of the models was evaluated using different statistical measured. The obtained results showed that the daily freshwater yield, energy efficiency, and exergy efficiency of the MSS was enhanced by 34%, 34%, and 46% compared with that of CSS. The production cost per liter of the MSS is 0.015 $/L. Moreover, SVM outperformed other ML methods for both SSs based on different statistical measures.

Low-cost bilayered structure for improving the performance of solar stills: Performance/cost analysis and water yield prediction using machine learning