Mathematical Modeling of Thin-Layer Drying Kinetics of Piper aduncum L. Leaves
Bibliographic record
Abstract
As well as most agricultural products, some medicinal plants need to go through a drying process to ensure quality maintenance, however each product behaves differently. Therefore, the present study aimed to evaluate the drying kinetics of spiked pepper (Piper aduncum L.) leaves and determine their thermodynamic properties at different drying temperatures in laboratory scale. Leaves with initial moisture content of 78% w.b. (wet basis) were subjected to drying at temperatures of 40, 50, 60 and 70 ºC and air speed of 0.85 m s-1 in an experimental fixed bed dryer. The drying kinetics of the leaves was described by statistical fitting of mathematical models and determination of effective diffusion coefficient and activation energy. Enthalpy, entropy and Gibbs free energy were also evaluated for all drying conditions. It was concluded that, among the models evaluated, only Midilli and Valcam can be used to represent the drying of Piper aduncum leaves; the first for the two highest temperatures (60 and 70 ºC) and the second for 40 and 50 ºC. The activation energy was approximately 55.64 kJ mol-1, and the effective diffusion coefficient increase with the elevation of temperature. The same occurs with the values of Gibbs free energy, whereas the specific enthalpy and entropy decrease.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".