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Record W2038757598 · doi:10.1002/ceat.200700263

Numerical Investigation of Hydrocarbon Enrichment of Process Gas Mixtures by Permeation through Polymeric Membranes

2007· article· en· W2038757598 on OpenAlexaff
Nikhil Kawachale, Ashish Kumar, Deepak M. Kirpalani

Bibliographic record

VenueChemical Engineering & Technology · 2007
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsPermeancePermeationMembraneHydrocarbon mixturesPolysulfoneChemical engineeringHydrocarbonChemistryThermodynamicsMaterials scienceChromatographyOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Abstract In membrane‐based gaseous separations, a consensus on the distribution of the components of the permeate stream in the immediate membrane vicinity has yet to be reached and the possibility of an underestimation of selective gas permeance due to lack of gas mixing on the permeate side exists. In this work, a numerical study of the permeation of ethylene and propylene in their binary mixtures with nitrogen through a composite poly(dimethylsiloxane) coated polysulfone membrane was performed. Continuity and momentum equations, along with gas compressibility and permeance properties for individual species of the gas mixture, were introduced into a comprehensive computational fluid dynamics (CFD) model. Simulation results showed that irrespective of the stage‐cut, gases with higher selectivity were well mixed in the vicinity of the membrane on the permeate side.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.210
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations10
Published2007
Admission routes1
Has abstractyes

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