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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 OpenAlex
Nikhil Kawachale, Ashish Kumar, Deepak M. Kirpalani

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

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.783

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 head0.004
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