A comparative approach was carried out between artificial neural networks (ANNs) and response surface methodology (RSM) to optimize the drying parameters during infrared–convective drying of white mulberry. The drying experiments were performed at different air temperatures (40°C, 55°C, and 70°C), air velocities (0.4, 1, and 1.6 m/s), and three levels of infrared radiation power (500, 1000, and 1500 W). RSM focuses on the maximization of effective moisture diffusivity ( ) and minimization of specific energy consumption ( ) in the drying process. The optimized conditions were encountered for the air temperature of 70°C, the air velocity of 0.4 m/s, and the infrared power level of 1464.57 W. The optimum values of and were 1.77 × 10⁻⁹ m²/s and 166.554 MJ/kg, respectively, with the desirability of 0.9670. Based on the statistical indices, the results showed that the feed and cascade-forward back-Propagation neural systems with application of Levenberg-Marquardt training algorithm and topologies of 3–20-20-1 and 3–10-10-1 were the best neural models to predict and , respectively. This finding suggests that the ANN as an intelligent method with better performance compared to the RSM can be used to predict the drying parameters of the infrared-convective drying of white mulberry fruit.

This study deals with the optimization of energetic and exergetic parameters of a hybrid-solar dryer to dry basil leaves under determined experimental conditions at three air temperatures (40 °C, 55 °C, and 70 °C) and three bed thickness levels (2, 4, and 6 cm). The optimization of the thermodynamic parameters was performed using the response surface method (RSM) based on the central composite design (CCD) and the desirability function (DF) to maximize the drying rate, exergy efficiency, improvement potential rate and the sustainability index, and to minimize the energy utilization, energy utilization ratio and exergy loss rate. These parameters were calculated on the basis of the first and second laws of thermodynamics as the response variables. Based on the results obtained, it was determined that the optimal conditions for basil drying were at a drying air temperature of 63.8 °C and a bed thickness of 2 cm. At this point, the parameters of the drying rate, energy utilization, energy utilization ratio, exergy efficiency, exergy loss rate, improvement potential rate and sustainability index were obtained with the maximum utility function (D = 0.548) as 0.27, 0.019 (kJ/s), 0.23, 65.75%, 0.016 (kJ/s), 1.10 (kJ/s) and 0.015, respectively.

This study aimed to examine the energy and exergy indices of the rosemary drying process in a hybrid-solar dryer (HSD) and the effects of air-drying parameters on these thermodynamic indices. Drying experiments were carried out at four levels of air temperature (40, 50, 60, and 70 ${}^{\circ }$C) and three levels of air velocity (1, 1.5, and 2 m/s). Energy and exergy were calculated by application of the first and second laws of thermodynamics. Based on the principal laws, energy efficiency, exergy losses, and exergetic improvement potential rate, were evaluated. The results showed that the energy utilization ratio (EUR) ranged from 0.246 to 0.502, and energy utilization (EU) ranged from 0.017 to 0.060 (kJ/s). Exergy loss and efficiency varied from 0.009 to 0.028 (kJ/s) and from 35.08% to 78.5%, respectively, and increased with increased temperature and air velocity. It was found that the exergy loss rate was affected by temperature and air velocity because the overall heat transfer coefficient was different under these conditions. By comparison, with increasing temperature and air velocity, the exergy efficiency increased. Because most energy is used to evaporate moisture, this behavior may be explained by improved energy utilization. The drying chamber sustainability index ranged from 0.0129 to 0.0293. This study provides insights into the optimization process of drying operations and operational parameters in solar hybrid dryers that reduce energy losses and consumption.