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Convective hot air drying technology is widely used for fruits and vegetables drying process due to its simplicity in usage, small investment, and convenience in operation. However, it is generally considered energy-intensive process with lower drying efficiency and quality because of unprecise control of drying process. In order to solve these problems, step-down relative humidity (RH) hot air drying technology was applied for papaya drying to improve drying efficiency and quality and reduce energy consumption. The effect of constant RH (20%, 30%, 40%, and 50%) and step-down RH of 50% with different holding times (10, 30, 60, and 90 min) and then decrease to 20% on heat and mass transfer process, drying quality, and energy consumption was examined. Results showed that decreasing RH contributed to improvement in the drying rate (DR). Compared to constant RH of 20%, the drying time under initial RH of 50% for 30 min followed by 20% RH shortened by 21.4%. Additionally, under this RH control strategy, the rehydration ratio (RR) and energy consumption achieved their maximum (7.71 ± 0.06) and minimum value (0.015 ± 0.001 kJ/g), respectively. During initial drying stage, high RH improved convective heat transfer coefficient (ht) values and material temperature increased quickly. Furthermore, high RH contributed to maintaining pore structure formation, which benefited moisture diffusion. When RH was reduced, low RH enhanced convective mass transfer coefficient (hm) significantly, resulting in rapid diffusion of internal moisture at high temperature, as well as rapid moisture evaporation from the surface. Therefore, step-down RH hot air drying was verified to improve drying of papaya slices, making it energy efficient process. The findings in current work indicate step-down relative humidity strategy is a promising technique to enhance hot air drying process, quality, and energy efficiency of papaya slices. [ABSTRACT FROM AUTHOR]

In current work, the energy and exergy analysis of hot air impingement drying mushroom slices was conducted under different air temperature (55, 60, 65, 70, and 75 °C), air velocity (3, 6, 9, and 12 m/s), and sample thickness (6, 9, and 12 mm) by the first and second law of thermodynamics. The statistical analysis results indicated that the effect of air velocity and temperature on the energy and exergy was more important than the sample thickness on it. The energy utilization and energy utilization ratio were in the range of 0.067–0.859 kJ/s and 0.087–0.34, respectively. The exergy loss and exergy efficiency was varied from 0.045–0.571 kJ/s and 0.315–0.879, respectively. Besides, the artificial neural network (ANN) was employed to predict the energy and exergy parameters. The modeling results revealed that the ANN models with arranged training algorithms and transfer function could be utilized to predict the performance of hot air impingement drying system with satisfactory accuracy. [ABSTRACT FROM AUTHOR]