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Record W4295779220 · doi:10.1016/j.ccst.2022.100069

Modeling of CO2 absorption into 4-diethylamino-2-butanol solution in a membrane contactor under wetting or non-wetting conditions

2022· article· en· W4295779220 on OpenAlexaff
Cuiting Yuan, Linlin Li, Yifu Li, Zhen Pan, Na Zhang, Tohid N. Borhani, Zhien Zhang

Bibliographic record

VenueCarbon Capture Science & Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWettingDecarburizationDiethanolamineAlkanolamineChemistryHollow fiber membraneSolventChemical engineeringVolumetric flow rateContactorAbsorption (acoustics)Materials scienceContact angleMembraneAnalytical Chemistry (journal)ChromatographyThermodynamicsAqueous solutionOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

In this work, 4-diethylamino-2-butanol (DEAB) as a new type of alkanolamine solvent is used for CO2 capture in a hollow fiber membrane contactor (HFMC). A model describing the gas and liquid reactions and transport inside the membrane contactor under the wetting or non-wetting conditions was built. The countercurrent flow of natural gas and solvent was considered in the model. To investigate the influence of solvent type on decarburization efficiency, DEAB was used and compared with other common solvents such as potassium carbonate (K2CO3), triethylamine (TEA) and diethanolamine (DEA). Under the same operating conditions, the impact of parameters such as humidity, gas flow rate, liquid concentration, membrane length on the decarburization performance was examined. The results indicate that DEAB solvent had the best overall performance especially under the wetting conditions. It was noted that increasing liquid concentration, membrane length and decreasing gas flow rate enhanced decarburization.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.518
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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