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Record W2052204136 · doi:10.1021/ie990539+

CFD Modeling of Mass-Transfer Processes in Randomly Packed Distillation Columns

2000· article· en· W2052204136 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.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2000
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPacked bedMass transferDistillationComputational fluid dynamicsStructured packingChromatographyProcess engineeringFractionating columnChemistryMaterials scienceChemical engineeringEnvironmental scienceThermodynamicsEngineeringPhysics

Abstract

fetched live from OpenAlex

The volume-averaged equations for velocity and concentration fields have been used to simulate the hydrodynamics and mass-transfer processes in randomly packed distillation columns. This approach is regarded as a second-generation computational fluid dynamics (CFD) based model, and a significant departure from the traditional one-dimensional, first-generation models. The model has ability to capture radial and axial variations in flow and mass-transfer conditions. The spatial variation of void fraction has been included to take into account the effect of bed structures. The simulation results have been compared with experimental data reported by Fractionation Research, Inc. (FRI) which performed their tests in a 1.22-m-diameter column with a packed bed height of 3.66 m. For validation, we have used data obtained with 15.9-, 25.4-, and 50.8-mm metal Pall rings at various operating conditions. Good agreement between CFD predictions and published experimental data has been obtained. This is regarded as an encouraging sign that CFD models can play a useful role in studying separation processes.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.289
Teacher spread0.229 · 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