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Record W2023028114 · doi:10.1021/ie0306067

Characterization and Comparison of the CO<sub>2</sub> Absorption Performance into Single and Blended Alkanolamines in a Packed Column

2004· article· en· W2023028114 on OpenAlexafffund
Adisorn Aroonwilas, Amornvadee Veawab

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2004
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDiethanolamineAlkanolamineMass transferAbsorption (acoustics)ChemistryAqueous solutionPacked bedMass transfer coefficientCarbon dioxideAnalytical Chemistry (journal)ChromatographyChemical engineeringMaterials scienceOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

The performance of carbon dioxide (CO 2 ) absorption into aqueous solutions of single and blended alkanolamines was evaluated experimentally in a bench-scale absorber packed with high-efficiency packings. The absorption experiments were conducted under atmospheric pressure, using a feed gas mixture containing 10% CO 2 and 90% nitrogen. Monoethanolamine (MEA), diethanolamine (DEA), diisopropanolamine (DIPA), methyldiethanolamine (MDEA), 2-amino-2-methyl-1-propanol (AMP), and their mixtures including MEA−MDEA, DEA−MDEA, MEA−AMP, and DEA−AMP were tested in this work. The absorption performance was presented in terms of the CO 2 removal efficiency, absorber height requirement, effective interfacial area for mass transfer, and overall mass-transfer coefficient ( K G a e ). Comparison of the absorption performance between the tested alkanolamines was made over ranges of operating conditions to establish the correlation between single- and blended-alkanolamine systems.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

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 communication0.0010.000
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 head0.039
GPT teacher head0.268
Teacher spread0.229 · 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

Citations297
Published2004
Admission routes2
Has abstractyes

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