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Record W6983790004

NUMERICAL SIMULATION OF FLUIDIZED BED DRYING OF PHARMACEUTICAL POWDERS USING A TWO-PHASE MODEL

2023· dissertation· en· W6983790004 on OpenAlexfundno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicMicroencapsulation and Drying Processes
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFluidized bedGranulationAgglomerateMoistureMass transferWork (physics)Process (computing)FluidizationWater content
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.036
GPT teacher head0.263
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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