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Record W2772829790 · doi:10.1002/aic.16044

Simulation of gas separation using partial element stage cut modeling of hollow fiber membrane modules

2017· article· en· W2772829790 on OpenAlexafffund
Sina Gilassi, Seyed Mohammad Taghavi, Denis Rodrigue, Serge Kaliaguine

Bibliographic record

VenueAIChE Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHollow fiber membraneSeparation processGas separationFraction (chemistry)MembranePermeationProcess (computing)Mole fractionFiberFlash (photography)Process simulationSeparation (statistics)Membrane technologyFlash evaporationStage (stratigraphy)Process engineeringMaterials scienceAnalytical Chemistry (journal)ChemistryChromatographyEngineeringComputer scienceComposite materialPhysicsOptics

Abstract

fetched live from OpenAlex

A mathematical model is developed to simulate a gas separation process using a hollow fiber membrane module. In particular, a new numerical technique is introduced based on flash calculation. Such analysis allows identifying the required membrane properties needed to reach module performance of interest. This model is validated for six different gas separation cases taken from literature. Then, the validated model is used to investigate the effect of O 2 and N 2 permeances on O 2 recovery and O 2 mole fraction in the permeate stream. A realistic two‐stage air enrichment process is also proposed for O 2 production using an industrial module with different fibers numbers. Moreover, this model is used to simulate a natural gas purification process using a single unit to determine the required membrane separation area and CH 4 loss. Finally, a two‐stage process is proposed to equally enhance CH 4 retentate mole fraction and decrease CH 4 loss. © 2017 American Institute of Chemical Engineers AIChE J , 64: 1766–1777, 2018

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.050
GPT teacher head0.323
Teacher spread0.272 · 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 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

Citations28
Published2017
Admission routes2
Has abstractyes

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