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Experimental and Modeling study of the CO 2 absorption performance into DEEA solution in membrane contactor

2021· preprint· en· W3164132955 on OpenAlexaff
Yihan Yin, Aoqian Qiu, Hongxia Gao, Zhiwu Liang, Wilfred Olso, Paitoon Tontiwachwuthikul

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsContactorWettingHollow fiber membraneMass transferMultiphysicsDimensionless quantityMass transfer coefficientMembraneAbsorption (acoustics)Materials sciencePhase (matter)ChemistryAnalytical Chemistry (journal)FiberChromatographyThermodynamicsComposite materialFinite element methodPhysics

Abstract

fetched live from OpenAlex

In this study, the absorption process of the aqueous DEEA solution for CO2 capture in polytetrafluoroethylene hollow-fiber membrane contactor was investigated by both experiment and simulation. Based on the finite element analysis method, a two-dimensional steady-state mathematical model was established using COMSOL Multiphysics simulation software to calculate the CO2 mass transfer flux (JCO2) of DEEA in the hollow fiber membrane contactor under non-wetting and partial wetting conditions and the distribution of CO2 concentration under corresponding conditions. The results show that the predicted JCO2 under 15% membrane wetting conditions is in good agreement with the experimental value, and the mass transfer performance is severely reduced under wetting conditions. In addition, a dimensionless equation was developed to predict the liquid phase, gas phase and membrane phase mass transfer coefficient and JCO2. The calculated JCO2 values are in good agreement with the experimental values with the average relative deviation (AARD) of 9.4%.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.001
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 head0.021
GPT teacher head0.244
Teacher spread0.223 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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