Development of a dimensionless model for simulating key parameters in solar distillation systems

Solar desalination systems are highly influenced by fluctuating meteorological conditions, yet many existing models fail to capture the combined effects of key environmental parameters. This highlights a research gap addressed by the present study, which developed a dimensionless simulation model to assess the interplay of ambient air temperature, solar radiation, wind velocity, and stage temperature on freshwater production. The model was validated using empirical data collected under average solar radiation levels of 200 to 225 W/m2, with a focus on stage 5 as the most productive stage. The initial predictive model yielded poor agreement with experimental results, with coefficient of determination (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) values of − 3.292, 74.77, and 29.58, respectively. However, incorporating the iterative heating term (IHT) significantly improved model accuracy, yielding an R2 of 0.953, RMSE of 7.80, and MAPE of 5.01, demonstrating a close fit and high reliability. Key findings showed a strong correlation between the ambient air temperature and stage temperature. The warmer ambient conditions increased stage temperature and freshwater yield while lowering heating demand. Consistent solar radiation profiles facilitated steady evaporation, whereas strong winds under intermittent radiation led to excessive cooling and reduced vapour production. The model demonstrated strong simulation capability, with a maximum deviation of 17% from empirical values. Real-time variations in solar input and peak radiation were more influential than daily averages in determining the distillate. The refined model accurately captures the system behaviour and is well suited for use in automated and off-grid desalination setups. Future work should explore dynamic model adaptation and regional calibration to enhance system robustness in diverse and variable climatic conditions.