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Record W2095258729 · doi:10.1002/cjce.5450780303

Experimental studies of liquid flow maldistribution in a random packed column

2000· article· en· W2095258729 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2000
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDistributorPacked bedVolumetric flow rateLiquid flowFlow (mathematics)Materials scienceScalingSurface tensionViscosityChromatographyMechanicsChemistryThermodynamicsComposite materialMathematicsPhysicsGeometry

Abstract

fetched live from OpenAlex

Abstract Liquid flow distribution has been a major concern when scaling up random packed columns. This study concerns the measurements of liquid flow distribution in a large scale column packed with 25.4 mm stainless steel Pall rings. The liquid flow distribution was studied with packed bed height from 0.9 to 3.5 m, liquid flow rate from 2.91 to 6.66 kg/m 2 ·s, and gas flow rate from 0 to 3.0 kg/m 2 ·s. In addition, three systems, water/air, aqueous detergent solution/air and Isopar/air, were used to study the effect of liquid physical properties on liquid flow distribution, and two different liquid distributors were employed to test the effect of liquid distributor design. It was found that liquid flow distribution was strongly influenced by liquid distributor design, packed bed height, gas flow rate and liquid viscosity, slightly influenced by liquid flow rate, but not by liquid surface tension.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.342

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 head0.007
GPT teacher head0.195
Teacher spread0.188 · 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