INVESTIGATING THE PERFORMANCE OF A FLAT-PLATE SOLAR WATER-HEATING SYSTEM USING CeO2/WATER NANOFLUID: A HOLISTIC APPROACH

Abstract

The efficiency of a solar water heater with a flat-plate collector, which had an absorber area of 2 m² and utilized a cerium oxide-water nanofluid as the working medium with a fixed volume fraction of 0.01%, was analyzed using a holistic approach. To assess the impact of flow rate on its heat transfer characteristics, the working fluid was systematically circulated through a pump-circulation system at various flow rates ranging from 1 to 3 liters per minute (LPM). The system underwent analytical and field testing, which revealed that it achieves a maximum thermal efficiency of 78.2% at a flow rate of 2 LPM. The heat transfer coefficient underwent a notable enhancement, leading to a 21.5% increase in thermal efficiency compared to a system operating with plain water. A refined artificial neural network (ANN) model was created to forecast thermal performance and outlet temperature. The optimized ANN model is structured with a 3-10-2 architecture and achieves a mean squared error of 8.2 × 10^-3. The performance of the optimized ANN model was assessed by testing its generalization capacity against experimental results. It was determined that the ANN model predictions aligned closely with the experimental data, exhibiting correlation coefficients ranging from 0.96 to 0.98. This study conclusively demonstrates that the analytical model and ANN can serve as a highly effective design tool for evaluating the performance of innovative solar collectors.