Hot air assisted solar greenhouse dryers: Improving drying performance by numerical, experimental, and artificial intelligence approaches

Drying is a process of heat and mass transfer, and solar drying of agricultural products is one of the oldest methods of food preservation. However, solar drying seriously reduces food quality due to environmental factors. For this reason, special artificial dryers minimize drying time and provide a better-quality, cleaner product with a long shelf life. In this study, a solar air collector (SAC) assisted greenhouse dryer was designed to improve the quality and performance of food drying. Three different greenhouse geometries were classified, and, using computational fluid dynamics (CFD) analysis, the greenhouse geometry with the most suitable Air velocity and temperature distribution in the drying process greenhouse was determined and manufactured. In the SAC-assisted greenhouse dryer, 10 mm thick oval-cut apple slices were dried under the climatic conditions of Elazig, Türkiye. Heat transfer coefficients and drying efficiencies were calculated for the drying processes with and without SAC. The performance of both drying methods was compared. CFD analysis was used to simulate the temperature distributions on the product surface in both drying methods and determine the best greenhouse dryer designs. In the SAC-assisted system, a product moisture content of 55.38 % and a moisture content of 0.09859 were achieved in 90 min less time, thus reducing the drying time by 41 % and significantly increasing the drying efficiency. The product surface temperature (Tp) and convective heat transfer (hc) values of the SAC-supported system were 4.8 °C and 2.27 W/m2 °C higher than those of the other system, respectively. In addition, mathematical equations were obtained using the Pace regression method for Tp, greenhouse dryer temperature (Tg), and moisture content (MC), which are essential parameters in the drying process. When the outputs of the models with Pace are benchmarked with the experimental results, mean absolute error (MAE) values of 0.18, 0.15, and 0.08 are obtained, respectively. As a result, this study includes different methods for solar greenhouse dryers, and detailed research was carried out using numerical, artificial intelligence, and experimental applications.