Prediction of Segregation Tendency Occurrence in Dry Particulate Pharmaceutical Mixtures: Development of a Mathematical Tool Adapted for Granular Systems Application
Bibliographic record
Abstract
Segregation phenomena are of importance in nearly all processes involving dry granular and powder mixtures. The extent of segregation directly influences the eventual rejection of a considerable percentage of the final product in the majority of pharmaceutical processes; among these are those mixtures destined for powder compression processing for the production of tablets. Although the parameters influencing segregation are relatively well-known qualitatively, there are, so far, no widely accepted quantitative prediction tools that permit process improvement and optimization of production as a function of the mixture's composition and the particulars of individual processes (e.g., geometry of the vessels). Thus, within present practice, only general design considerations and the technical expertise of engineers and operators are relied upon to optimize these processes on a case-by-case basis. It is in these circumstances that a study of the tendency towards segregation in free flowing granular materials was conducted, using a simple tool previously developed for the study of the behavior of continuous chemical reactors with classical fluid flows. The measurement of average residence times and their variance is used to calculate the deviation of chemical reactors from the ideal behavior of a perfectly mixed vessel or a plug flow pattern. In this work, these measurements are adapted to evaluate the tendency of a granular mixture to segregate. The method consists of introducing a pulse perturbation (of another material) to the established regular flow of a single granular material or a granular mixture and to then calculate the response of the system in terms of the concentration of the pulsed material at the process outlet. The average granular particle residence time and its standard deviation are then related to the segregation tendency.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".