Investigation of a solar still behaviour using response surface methodology

This work proposes an innovative statistical model by utilizing the response surface methodology (RSM) method to analyze a batch solar behaviour still. This investigation's main goal is to study the impact of the input factors (solar radiation, ambient temperature, water depth, and thickness of insulation) most influencing water productivity. The polynomial regression model derived from a numerical balance energy model to predict the solar's productivity still is established. The quadratic model is checked by a coefficient of determination (R-2). An excellent fitting is attained between the forecasted results derived from the statistical model and the numerical simulation derived from the heat balance model. The results reveal that the importance of the influence in the order of impact on the amount of distilled water is water depth, solar radiation, ambient temperature, and thickness of insulation. A simple polynomial statistical model is stated in this investigation to determine and maximize the amount of distilled water from solar still based on the four considered input factors.

Automated summary

This study proposes an innovative statistical model using the response surface methodology to analyze a batch solar behavior still. It determines the most influential input factors affecting water productivity, establishes a polynomial regression model for prediction, and validates the model's fitting. The results indicate the importance ranking of water depth, solar radiation, ambient temperature, and thickness of insulation on distilled water quantity from solar stills.