MétaCan
Menu
Back to cohort
Record W2076559272 · doi:10.1021/ie030347l

Geometry-Based Model for Predicting Mass Transfer in Packed Columns

2003· article· en· W2076559272 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 · 2003
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPacked bedMass transferStructured packingPressure dropMechanicsMaterials scienceDistillationPenetration (warfare)GeometryFlow (mathematics)ChemistryChromatographyMathematicsPhysics

Abstract

fetched live from OpenAlex

We have extended our previous work on the modeling of geometry, liquid trickle flow, and pressure drop to study the mass-transfer process in a packed column. On the basis of the penetration theory and the detailed information of lateral and axial variations in packing geometry and fluid dynamics from our previous models, a predictive mass-transfer model has been developed on the scale much smaller than a packing particle in the case of random packings or a flow channel in the case of structured packings. Distillation experiments have been carried out with methanol/2-propanol and methanol/water for 16 mm metal Pall rings. The model has been validated with the experimental results and our previous data on a novel vertical-sheet structured packing. Simulations for uniform and uneven initial distributions have been carried out, which showed a strong influence of liquid-flow distribution on mass-transfer efficiency in a packed column.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.598
Threshold uncertainty score0.833

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.093
GPT teacher head0.308
Teacher spread0.215 · 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