Comparison of the energy and exergy parameters in cantaloupe (Cucurbita maxima) drying using hot air

Drying is one of the common techniques for preserving agri-food product quality. However, for each product, the appropriate drying parameters should be identified to optimize drying quality and energy consumption. The present work aims to explore the performance of a hot air dryer (HAD) to dry cantaloupe (Cucurbita maxima) slices at three temperatures (50, 60, and 70 degrees C). The effects of drying temperature/duration on drying kinetics, energy, and exergy parameters of cantaloupe slices were investigated. The obtained data indicated a decrease in drying time and specific energy consumption (SEC) with temperature. On the other hand, the effective moisture diffusivity (D-eff), energy utilization (EU), energy utilization ratio (EUR), exergy loss, exergy efficiency, exergetic improvement potential (EIP) and sustainability index (SI) increased with temperature. SEC, D-eff, EU, EUR, exergy loss, exergy efficiency, EIP, and SI were in the range of 85.48-139.77 MJ/kg, 2.91 x 10(-12)-6.18 x 10(-12) m(2)/s, 0.0207-0.0925 kJ/s, 0.1951- 0.8703, 0.0088-0.0447 kJ/s, 0.2839-0.9239, 0.0047-0.0117 kJ/s and 3.0880-3.8540, respectively. Moreover, adaptive neuro-fuzzy inference systems (ANFISs) and artificial neural networks (ANNs) were used as two state-of-the-art intelligent algorithms to predict the drying dynamics of cantaloupe slices in HAD and the performance of both methods was found to be reliable (R-2 > 0.97). Indeed, ANFIS provided better performance for predicting energy utilization, energy utilization ratio, and exergy loss with R-2 values of 0.9919, 0.9961, and 0.9939, respectively. On the other hand, ANN outperformed ANFIS in predicting exergy efficiency and moisture ratio by achieving an R-2 value of 0.9999 for both parameters. The authors believe the outcomes of the present study can be used as a framework for choosing efficient drying parameters for drying cantaloupe or similar fruits in HAD systems.