A Mathematical Model for Dehydration by Successive Pressure Drops: Simulation of Discarded Potatoes Dehydration
Bibliographic record
Abstract
Dehydration by Successive Pressure Drops (SPD) is a process with high potential for treating heat-sensitive materials; this includes agro-industry by-products. However, the response of drying kinetics to operating conditions of SPD is not fully understood. The present manuscript refers to mathematical simulations to describe drying kinetics of discarded potatoes using SPD. While there are numerous theoretical, semi-theorical and empirical mathematical models, the selection of the appropriate model is a rigorous process. In this paper, the Thompson thin-layer semi-theoretical model was chosen since the assumptions for this model (e.g., product is arranged in thin layers) are fulfilled for the SPD. As a result of mathematical simulation, it was possible to describe the drying kinetics in terms of the major parameters of SPD namely pressurizing level (Pa) and the Frequency of Pressure Drop (FPD). The dehydration by SPD allows the removal of water from the material, mainly at the pressure drop. If this stage occurs more frequently, the drying rate increases. The model developed describes at less 91% of the variability of the experimental data. It is recommended to use high FPD and Pa equals to 0.50 MPa.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".