Effective <scp>Maxwell−Stefan</scp> diffusion model of near ambient air drying validated with experiments on Thomson seedless grapes
Bibliographic record
Abstract
Abstract With 8% of the world's grapes being dried into raisins, grape drying constitutes a sizable part of agricultural revenue. The drying process in the developing regions of the world struggles with a lack of reliable drying techniques and rising energy costs. In this work, we report a systematic study to elucidate the intrinsic drying kinetics of Thomson seedless grapes. The grapes are subjected to drying by warm air (40−55°C) rather than hot air (>60°C), which has the advantage of producing the sought‐after green raisins. The air velocity is varied between 0.05 and 0.15 m/s to isolate the effect of external mass transfer resistance. A 1D mathematical model was developed that incorporates the effects of internal diffusion and external mass transfer resistance as well as the effects of temperature and relative humidity on the kinetics and driving forces. In addition, the model incorporates a novel thermodynamic model to describe the vapour pressure of water at the surface of the grape. The uniqueness of the model is that it combines thermodynamics and mass transfer in a single framework. Therefore, the parameters that describe the evaporation are the same parameters that describe the internal diffusion within the matrix, making the model robust. This robust, experiment‐trained model can be reliably transferred to develop detailed computational protocols to model cost‐effective grape drying processes in the field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".