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Record W2043942130 · doi:10.1002/aic.690460407

Pressure swing permeation: Novel process for gas separation by membranes

2000· article· en· W2043942130 on OpenAlexaff
Xianshe Feng, Chuen Y. Pan, John Ivory

Bibliographic record

VenueAIChE Journal · 2000
Typearticle
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPermeationPressure swing adsorptionMembraneChemistrySwingCabin pressurizationAdsorptionGas compressorChemical engineeringChromatographyProcess engineeringMaterials scienceEngineeringComposite materialOrganic chemistryMechanical engineering

Abstract

fetched live from OpenAlex

Abstract A novel process for gas separation, called pressure swing permeation, was investigated to elevate the relatively low permeate pressure by pressurization with high‐pressure feed gas, thereby reducing or eliminating additional permeate compression costs where a pressurized permeate is required. This process uses two or more membrane modules and operates in a cyclic fashion, with each module repeatedly undergoing the sequential steps of feed admission and permeation, residual removal, permeate reception, permeate pressurization, and product withdrawal. The unsteady‐state permeation associated with pressure swing permeation was studied parametrically, and a bench‐scale unit compromising two hollow‐fiber membrane modules in parallel was tested for H 2 /N 2 separation to demonstrate the effectiveness of the process. The permeate product at a pressure as high as the feed pressure can be produced without using a compressor. This is impossible with traditional steady‐state processes where a pressure differential across the membrane must be maintained. The pressure swing permeation is analogous to pressure swing adsorption and has the potential to be synergistically integrated with the pressure swing adsorption process for enhanced separation of gases.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score1.000

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)0.0010.000

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 head0.014
GPT teacher head0.273
Teacher spread0.259 · 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.

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

Citations21
Published2000
Admission routes1
Has abstractyes

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