Enhancement and prediction of a stepped solar still productivity integrated with paraffin wax enriched with nano-additives

The present study deals with the emhancement of thermophysical properties of paraffin wax using Silver nanoparticles and to study the feasibility of its application in a stepped solar still through an experimental approach. Along with the experimentation, the yield, temperature of water are predicted using a machine learning approach. The thermophysical properties such as melting temperature, latent heat, thermal conductivity and thermal stability of the paraffin wax using different concentrations (1 and 2%) are investigated and compared to that of paraffin wax without nanoadditives. The thermal conductivity of paraffin wax was enhanced by about 35.71% and 78.57% using nano-additives concentrations of 1% and 2%, respectively. Three different stepped SS namely, SS with paraffin wax, SS with paraffin wax doped with Ag nanoparticles, and SS without paraffin wax are fabricated and tested for the climatic conditions of Coimbatore, India. From the experimental results of fresh water generation, it is identified that the SS using nanocomposite PCM and PCM without nanoadditives are enhanced by about 75.65% and 114.81% respectively, while compared to the SS without any thermal energy storage. In order to estimate the amount of water that can be produced by each of the three solar stills, a single adaptive neuro-fuzzy inference system (ANFIS) and a hybrid adaptive neuro-fuzzy inference system-particle swarm optimizer (PSO) were used as machine learning models. According to the statistical assessment, the ANFIS-PSO model had a greater level of accuracy than that of the standalone ANFIS. ANFIS-PSO had very high determination coefficient ranged between 0.981 and 0.995 which indicates its capability to predict water yield of the tested solar stills.