Predictive modeling and optimization of tubular distiller operation using response surface methodology under silver nanomaterial infused PCM thickness variations

Abstract

This study introduces a novel mathematical model to predict freshwater production and temperature profiles within a tubular solar still (TSS) under varying conditions. Employing RSM (response surface methodology) with a four-factor, five-level central composite design, we evaluated the performance of an Ag-nanomaterial’s-improved phase changing material (PCM)-enhanced TSS. RSM effectively modeled the system, enabling optimization of yield (P) and water (Tw) and glass (Tg) temperatures across different PCM thicknesses. Regression models were developed using RSM to predict performance parameters, leading to the identification of optimal process conditions. PCM thickness was varied from 0 to 4 cm. Optimal conditions included a 1.34 cm PCM thickness, 40 °C ambient temperature, 0.73 m/s air speed, and 720 W/m 2 radiation. In this case the expected optimum responses of productivity, 5931.15 mL/m 2 .d. The RSM models demonstrated high accuracy and consistency with experimental data, validating the approach. These findings highlight the potential of RSM for enhancing solar distillation system performance. 1. Productivity and temperatures of water and glass of tubular still are predicted using RSM. 2. The effect of different PCM thickness is tested using RSM. 3. Prediction process is function of environmental parameters and nanoparticles-PCM thickness. 4. Optimum values of parameters obtained from the response surface model were predicted. 5. The optimized variable settings are as follows: Nano-PCM thickness (C) at 1.37 %, ambient temperature set at 40 °C, wind velocity maintained at 0.73 m/s, and solar irradiation at 720 w/m 2 .