Experimental and theoretical evaluation of thermophysical properties for moist air within solar still by using different algorithms of artificial neural network

Abstract

In this research article, an attempt has been made to predict the thermophysical properties of moist air in a solar still cavity with the help of Artificial Neural Network (ANN) modelling. Six training algorithms have been used to train, test, and validate the ANN model (viz. OSS, CGP, CGF, RP, SCG, and LM). Water and inner glass cover temperatures were selected as the input parameters whereas, the model output are: thermal conductivity, partial vapour pressure at water & glass surface, thermal conductivity, volumetric expansivity, specific heat, latent heat of vaporization, and dynamic viscosity. The findings have revealed that the proposed ANN model can be used to predict the thermophysical properties of moist air with excellent accuracy. The results of ANN model were tested against the well-established relations of Tsilingiris. Out of all the training algorithms used LM was found to be the best in all the stages of ANN modelling, as the results are well within an accuracy level of more than 95%. Hence, the developed LM algorithm-based ANN model is one of the most suitable algorithm for the prediction of the thermophysical properties of moist air.