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Record W4290959601 · doi:10.1002/cjce.24485

Experimental investigation on drying performance of pharmaceutical granules in a pulsation‐assisted fluidized bed dryer

2022· article· en· W4290959601 on OpenAlexafffundvenue
Chen Li, Carter Blocka, Lifeng Zhang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
KeywordsFluidizationFluidized bedMaterials scienceAirflowVolumetric flow rateWork (physics)ChromatographyChemical engineeringMechanicsChemistryThermodynamicsOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Abstract Fluidized bed drying has been widely employed in pharmaceutical manufacturing processes. Due to the considerable cohesiveness of wet pharmaceutical granules, channelling phenomena pose significant challenges to fluidization and drying. In this work, the drying performance of pharmaceutical granules in a pulsation‐assisted fluidized bed dryer was experimentally investigated. The drying rate and energy efficiency were investigated with representative pharmaceutical powders, including active pharmaceutical ingredients (APIs). It is found that the pulsed airflow is effective in enhancing the drying rate at higher superficial gas velocity. A lower pulsation frequency is more favourable to improve the drying rate. During the constant rate period, energy efficiency is between 60% and 45% for the drying process, while the energy efficiency decreases to 10% during the falling rate period. A pulsed fluidized bed dryer has shown a higher energy efficiency than a conventional one with a constant air flow. Among nine thin‐layer drying models examined in this work, the Midilli and Kucuk model has shown the best agreement between the experimental data and the predicted results.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.124

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.221
Teacher spread0.179 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations8
Published2022
Admission routes3
Has abstractyes

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