A Quasi‐Steady State Shell and Shrinking Core Approach to the Drying of Porous Particles and an Example of Parameter Identification
Bibliographic record
Abstract
Abstract A quasi‐steady state shell and shrinking core approach which recognizes heat and mass transfer resistances in both the gas and particle phases for drying of a porous particle is proposed. A mean field model (constant properties) using this approach was embedded in a spreadsheet combined with a genetic algorithm for parameter identification to provide an easy means of characterizing the drying process from drying data. In drying, assuming a mean field, four major parameters are typically unknown: two related to the process (heat and mass transfer coefficients) and two which incorporate porous particle properties (shell thermal conductivity and vapour diffusivity). It is shown how these four parameters may be determined from experimental drying data. The model was applied to data for spouted bed drying of rice. For the particular case studied, external heat transfer was found to be the controlling mode, although resistance to moisture diffusion within the particle is important. The approach presented admits of future refinements to improve its scope and utility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".