NUMERICAL SIMULATION OF FLUIDIZED BED DRYING OF PHARMACEUTICAL POWDERS USING A TWO-PHASE MODEL
Bibliographic record
Abstract
Tablet production in the pharmaceutical industry is a common process for oral ingestion products. Before tablet production, mixtures of active pharmaceutical ingredients and other excipients are granulated, generally through the process of wet granulation. The wet granulation process agglomerates mixtures of powder components to create homogeneous granules, typically using water as a liquid binder. Before the wet granules can be made into tablets, they have to be dried to an acceptable level. Fluidized beds have been extensively used for the drying of these granules. To better understand the fluidized bed process, mathematical models have been created to emulate the drying phenomena. Simplified models, such as phenomenological models, aim to capture the drying characteristics without defing the more complex kinetics and thermodynamics of the system. A recent approach based on a two-phase model is further verified in this work using experimental data from a lab scale fluidized bed. The model was examined against the pharmaceutical powder moisture content and temperature profiles from previous experimental work. The model is comprised of the mass and energy balances of five distinct sections of the fluidized bed. Powder moisture and heat transfer are governed by a stagnant gas film in equilibrium with the surrounding gas. The model shows good correlation with the experimental data. It also displays the general characteristics of pharmaceutical powder drying, with distinct constant drying rate and falling drying rate periods. Upon model validation, optimization of the inlet gas parameters is explored. Optimization of the model is imple- mented, controlling the inlet gas parameters and incorporating a stepwise change during the drying process. It was found that a single stepwise change has a negligible effect on optimizing the process but is still useful around the end point of the batch. In addition, optimization results and behaviours are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".