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Record W2104171216 · doi:10.1002/aic.14678

CO<sub>2</sub> removal by single and mixed amines in a hollow‐fiber membrane module—investigation of contactor performance

2014· article· en· W2104171216 on OpenAlexaff
Ion Iliuta, Francis Bougie, Maria C. Iliuta

Bibliographic record

VenueAIChE Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCountercurrent exchangeMembraneHollow fiber membraneWettingIsothermal processContactorAbsorption (acoustics)DiffusionChemistryFiberChemical engineeringMaterials scienceChromatographyAnalytical Chemistry (journal)ThermodynamicsComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

This work investigates CO 2 removal by single and blended amines in a hollow‐fiber membrane contactor (HFMC) under gas‐filled and partially liquid‐filled membrane pores conditions via a two‐scale, nonisothermal, steady‐state model accounting for CO 2 diffusion in gas‐filled pores, CO 2 and amines diffusion/reaction within liquid‐filled pores and CO 2 and amines diffusion/reaction in liquid boundary layer. Model predictions were compared with CO 2 absorption data under various experimental conditions. The model was used to analyze the effects of liquid and gas velocity, CO 2 partial pressure, single (primary, secondary, tertiary, and sterically hindered alkanolamines) and mixed amines solution type, membrane wetting, and cocurrent/countercurrent flow orientation on the HFMC performance. An insignificant difference between the absorption in cocurrent and countercurrent flow was observed in this study. The membrane wetting decreases significantly the performance of hollow‐fiber membrane module . The nonisothermal simulations reveal that the hollow‐fiber membrane module operation can be considered as nearly isothermal. © 2014 American Institute of Chemical Engineers AIChE J , 61: 955–971, 2015

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.577

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 head0.008
GPT teacher head0.180
Teacher spread0.172 · 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

Citations53
Published2014
Admission routes1
Has abstractyes

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