Modeling and optimization of working conditions of pyramid solar still with different nanoparticles using response surface methodology

The present work introduces formulating a mathematical modeling to predict the thermal performance of pyramid solar distiller (PSD) using the technique of response surface methodology (RSM) to be applied in solar distillers under different environmental parameters and nanoparticle types and concentrations. The most influential climatic process parameters considered are solar intensity, ambient temperature, and wind velocity. The regression models for predicting the performance parameter responses were developed using RSM and a four-factor, five-level central composite architecture. The optimum parameters values obtained from RSM were predicted. The impact of various nanomaterials mixed with the water basin on PSD performance was studied. Three different nanomaterials were used (titanium oxide (TiO2), aluminum oxide (Al2O3) and copper oxide (Cu2O)). The selection of nanomaterials was considered according to their optical, thermophysical, and heat transfer properties. Effects of nanoparticles concentration on daily responses were studied. The ascertained optimal parameters were 19.5% Cu2O concentrations, 720 w/m(2) solar-intensity, 38.6 degrees C ambient temperature, and 0.5 m/s wind speed for achieving the maximum productivity of PSD. Besides, the average daily productivity of Cu2O-PSD, Al2O3-PSD and TiO2-PSD at nano-concentration 0.3% was 6150, 5720 and 5300 mL/m2.day compared to 3900 mL/m(2).day for that of conventional PSD. So, the average daily productivity increase of Cu2O-PSD, Al2O3-PSD and TiO2-PSD was 57%, 46% and 36% over PSD, respectively. Moreover, the error existed among the actual experimental and RSM coded values for P, Tw and Tg lies within 5.2%, 4.9%, and 6.5%, respectively. Evidently, this affirms the excellence of reproducibility of the pilot experimental results.