Statistical modelling of the spouted bed coating process using positron emission particle tracking (PEPT) data
Bibliographic record
Abstract
Abstract Coating of particles larger than about 1 mm can be achieved in a spouted bed, a particle mobilization device in which a strong particle circulation occurs, rapidly upwards in a lean central “spout” region and downwards in a slowly moving annular settled bed. In a spouted bed coater, a spray nozzle is placed at the base of the spout, spraying upwards into a distinct coating zone. The coating formation in a spouted bed is inter alia a function of (i) the particle motion, that is, how often and where particles enter and traverse the coating zone and (ii) the extent of droplet collection by individual particles passing through the coating zone. The coating model proposed here is based on the statistical history of individual particles, whose projected area governs the collection of spray droplets in the coating zone. Positron emission particle tracking (PEPT) has been used to determine the particle trajectories, the distribution of cycle times and the size and voidage of the spout. Whilst the model is not capable of delivering absolute values of coating mass a priori, it can predict deviations from a mean, which can itself be determined from an overall mass balance. To validate the model, a spouted bed coating process was studied in which coarse PVC spheres were coated with the hot‐melt coating material polyethylene glycol (PEG) 1500. Coating mass distributions, derived from the weight data of individual particles before and after manual coating removal, compared (for the studied conditions) very well with the predictions of the model.
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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 | 0.000 | 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 | 0.001 | 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".