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Record W2115082485 · doi:10.1002/ceat.201400606

Comparison of Overall Gas‐Phase Mass Transfer Coefficient for CO<sub>2</sub> Absorption between Tertiary Amines in a Randomly Packed Column

2015· article· en· W2115082485 on OpenAlexaff
Wen Le, Helei Liu, Wichitpan Rongwong, Zhiwu Liang, Kaiyun Fu, Raphael Idem, Paitoon Tontiwachwuthikul

Bibliographic record

VenueChemical Engineering & Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsMass transferMass transfer coefficientAmine gas treatingChemistryVolumetric flow rateInert gasAbsorption (acoustics)Packed bedAnalytical Chemistry (journal)InertRaschig ringStructured packingSolventChromatographyMaterials scienceThermodynamicsOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract The mass transfer performance of CO2 absorption into an innovative tertiary amine solvent, 1‐dimethylamino‐2‐propanol (1DMA2P), was investigated and compared with that of methyldiethanolamine (MDEA) in a packed column with random Dixon‐ring packing. All experiments were conducted under atmospheric pressure. The effects of inert gas flow rate, amine concentration, liquid flow rate, CO2 loading, and liquid temperature on mass transfer performance were analyzed and the results presented in terms of the volumetric overall mass transfer coefficient (KGav). The experimental findings clearly indicate that 1DMA2P provided better mass transfer performance than MDEA. For both 1DMA2P and MDEA solutions, the KGav increased with rising amine concentration and liquid flow rate, but decreased with higher CO2 loading. The inert gas flow rate only slightly affected the KGav. A satisfactory correlation of KGav was developed for the 1DMA2P‐CO2 system.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.017
GPT teacher head0.256
Teacher spread0.240 · 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 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

Citations41
Published2015
Admission routes1
Has abstractyes

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