MétaCan
Menu
Back to cohort
Record W4405747615 · doi:10.1016/j.ces.2024.121142

An additive approach toward determination of liquid-phase mass transfer coefficients in sandwich packings

2024· article· en· W4405747615 on OpenAlexaff
Patrick T. Franke, Ulrich Schlattmann, Oorv Devasthali, Nicole Lutters, Markus Schubert, Uwe Hampel, Ion Iliuta, Faı̈çal Larachi, Eugeny Y. Kenig

Bibliographic record

VenueChemical Engineering Science · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversité Laval
FundersDeutsche Forschungsgemeinschaft
KeywordsMass transferPhase (matter)ChromatographyChemistryMass transfer coefficientThermodynamicsMaterials sciencePhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

• New additive method to process measured mass transfer data in sandwich packings. • Novel liquid-side mass transfer coefficient correlations for structured and sandwich packings. • Liquid-side mass transfer is up to 4.5 times faster in froth flow compared to film flow. Sandwich packings are innovative separating column internals based on a periodic arrangement of two conventional structured packings with different geometrical surface areas. They are operated with partially flooded layers to intensify phase interactions and enhance mass transfer. The application of sandwich packings in absorption and distillation processes requires detailed understanding of the gas–liquid mass transfer phenomena in the individual layers. For this reason, we carried out an experimental investigation of CO 2 desorption and developed an additive approach to process the measured data. On this basis, liquid-side mass transfer correlations for the flow patterns in the flooded packings sections were derived. The novel additive approach together with the developed correlations allows accurate prediction of the liquid-side mass transfer in sandwich packings.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.625
Threshold uncertainty score0.676

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 integrity0.0000.000
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 head0.010
GPT teacher head0.260
Teacher spread0.250 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueChemical Engineering ScienceSame topicRheology and Fluid Dynamics StudiesFrench-language works237,207