Experimental and Numerical Study to Enhance Granule Control and Quality Predictions in Pharmaceutical Granulations
Bibliographic record
Abstract
Background/Objectives: The pharmaceutical industry demands stringent regulation of product characteristics and strives to ensure the reproducibility of granules manufactured via the wet granulation process. A systematic model employing the discrete element method (DEM) was developed herein to gain insights into and better control this process. Methods: The model comprehensively simulates particle behavior during granulation by considering the intrinsic properties of the powder material, the specific geometry of the granulation equipment, and various operational conditions, including impeller speed and chopper use. Notably, this approach can simulate dynamic interactions among particles and integrate complex phenomena, such as cohesion, which is crucial for predicting the formation and quality of granules. Results: To further support process optimization, an EDEMpy artificial intelligence (AI) tool was developed as a posttreatment routine to monitor and analyze agglomerate size distributions, proving essential for assessing the efficiency of the granulation process and the quality of resulting granules. The DEM model was evaluated by comparing its output with experimental data collected from a 0.5 L high-shear granulator. The model reproduced the granule growth kinetics observed experimentally, confirming the agreement between the experimental and numerical analyses. Conclusions: This underscores the model’s potential in predicting and controlling granule quality in wet granulation processes, enhancing the precision and efficiency of pharmaceutical manufacturing.
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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 | 0.000 | 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".