Hot-air Drying Kinetics of Yam Slices under Step Change in Relative Humidity
Bibliographic record
Abstract
Abstract The drying kinetics and mathematical modeling of hot-air drying of yam slices were investigated under two-stage relative humidity (RH) control strategy with 60 °C and 1.5 m/s as its constant drying temperature and air velocity, respectively. Results indicate high RH in the initial stage results in high sample’s temperature that enhances water diffusion in the falling rate drying period. Within the scope of current work, change in RH in the later drying period has insignificant influence on sample’s temperature rise while low RH can accelerate the drying rate. Compared to drying at constant 20 % RH at the same drying air temperature, the drying strategy of using 40 % RH over the first 15 min and then lowing to 20 % RH for the remainder time yields a shorter drying time. Weibull model adequately described the moisture content variation with time for all experiments with the scale parameter ranging from 105.02 to 122.38 min and the values of shape parameters from 0.988 to 1.183. The effective moisture diffusivity determined from the Weibull model varied from 2.032 to 2.610×10 −8 m 2 /s. The rehydration ratio increased as the overall drying time was reduced. Microstructure examination shows that higher RH in the initial drying stage can lead to a more porous microstructure which enhances drying, while fast drying rate in the initial drying period generates a crust layer which hinders drying.
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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".