Mathematical modeling and optimization of pyramidal still performance using response surface method and Ag-nanoparticle enhanced phase change material

This study aims to develop a robust mathematical model using the Response Surface Methodology (RSM) to predict the thermal efficiency of a pyramidal solar still (PSD). The model takes into account various environmental conditions and different concentrations of silver nanoparticles (Ag) combined with paraffin wax as a thermal storage material (PCM). The applicability of this model extends to various solar stills operating under diverse weather conditions and employing different nanoparticle concentrations. The study focuses on three crucial weather-related variables: solar irradiation, air temperature, and wind speed, all of which significantly influence the solar distillation process. By utilizing RSM with a central composite design featuring four factors and five levels, regression models are constructed to forecast the responses of various performance parameters. The optimal values for process parameters are determined using RSM, and the models are employed to predict the ideal conditions for maximizing the performance of the solar distillation system. These optimized conditions have the potential to enhance both productivity and efficiency. The investigation considers five different Ag concentrations (wt%): 0, 1, 2, 3, and 4 %. The optimized parameter settings are as follows: Ag-Nano concentration (C) at 2.78 %, ambient temperature set to 40 degrees C, wind velocity maintained at 0.5 m/s, and irradiation at 720 W/m2. The results show a high level of accuracy and consistency between the predicted values generated by the RSM models and the experimental data. The errors between the actual and coded values for the performance parameters (P, Tw, and Tg) fall within a narrow range, with errors of 5.2 %, 4.9 %, and 6.5 %, respectively. These findings underscore the effectiveness and reliability of the proposed mathematical modeling approach.