Comparison of the energy and exergy parameters in cantaloupe (Cucurbita maxima) drying using hot air
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".