ENHANCING SOLAR STILL EFFICIENCY: AN OPEN-SOURCE PYTHON ALGORITHM FOR ACCURATE PERFORMANCE PREDICTION AND DATA GENERATION

The present work focuses on optimizing solar stills to address global water scarcity, impacting 2.2 billion people, aligning with UN Sustainable Development Goal 6 for sustainable water management. Solar still desalination is particularly suited to off-grid applications due to its integration with renewable energy sources.
In the scientific literature, efforts to employ AI for predicting solar still performance are hindered by the scarcity of experimental data. To overcome this, we introduce an innovative open-source Python algorithm designed to optimize solar still designs. Validated with a precise 4% error margin, this model accurately forecasts performance and addresses data scarcity by generating a comprehensive dataset for enhanced machine learning training.
The algorithm employs the 4th-order Runge–Kutta (RK4) method to solve differential equations, calculating temperatures (water, cover, absorber, and insulation), cumulative condensed water flow, efficiency, and cost. It adjusts computations based on ambient temperature and solar irradiation data, utilizing interpolation techniques for increased precision.
Additionally, the algorithm provides a visualization of device configurations and includes detailed technical descriptions. This encompasses geometric features, meteorological conditions, environmental factors, and materials data stored in an adjustable dataframe. It calculates thermodynamic properties using equations of state from the IAPWS association for each iteration. Moreover, hydraulic considerations such as the Colebrook–White equation approximation via Newton’s method for turbulent regimes are integrated to estimate the Darcy friction factor for inclined, cascade, and stepped solar still configurations.
By optimizing parameters and materials, the algorithm enhances solar still efficiency while balancing cost-effectiveness. It minimizes resource expenditures and enriches machine learning training data, demonstrating potential for innovative, economically viable solar desalination solutions.