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Record W2549666781 · doi:10.1002/cjce.22745

Assessment of scale‐up dimensionless groups methodology of gas‐solid fluidized beds using advanced non‐invasive measurement techniques (CT and RPT)

2016· article· en· W2549666781 on OpenAlexvenueno aff
Abdelsalam Efhaima, Muthanna H. Al‐Dahhan

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
FundersMinistry of Education, Libya
KeywordsDimensionless quantityScale (ratio)Pressure dropMechanicsFluidized bedSCALE-UPWork (physics)Mixing (physics)Tracking (education)Materials scienceComputer scienceMechanical engineeringEngineeringPhysicsThermodynamicsClassical mechanics

Abstract

fetched live from OpenAlex

The most common scale‐up methodology for gas‐solid fluidized bed reactors that has been reported in the literature is based on matching the dimensionless groups. This scale‐up methodology in the literature has been validated by only measuring global hydrodynamic parameters (overall holdups and pressure drop, etc.) without details. Therefore, in this work, we have applied advanced non‐invasive measurement techniques, gamma‐ray computed tomography (CT) and radioactive particle tracking (RPT) techniques, for the first time to evaluate such scale‐up methodology by measuring local hydrodynamic parameters. The results obtained demonstrate that the reported set of the dimensionless groups are not adequate in capturing all the interplay phenomena for achieving similarity in the local hydrodynamic parameters when the proposed set of dimensionless groups has been matched using two sizes of fluidized beds of 0.14 m and 0.44 m and sets of operating conditions. This finding confirms that the local measurements of the hydrodynamic parameters are essential for detailed assessment of scale‐up methodologies.

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)0.001
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 score0.020
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.026
GPT teacher head0.252
Teacher spread0.226 · 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

Citations20
Published2016
Admission routes1
Has abstractyes

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