| Popis: |
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 °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 (Deff), energy utilization (EU), energy utilization ratio (EUR), exergy loss, exergy efficiency, exergetic improvement potential (EIP) and sustainability index (SI) increased with temperature. SEC, Deff, EU, EUR, exergy loss, exergy efficiency, EIP, and SI were in the range of 85.48–139.77 MJ/kg, 2.91 × 10−12–6.18 × 10−12 m2/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 (R2 > 0.97). Indeed, ANFIS provided better performance for predicting energy utilization, energy utilization ratio, and exergy loss with R2 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 R2 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. |
