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Record W2082018554 · doi:10.1016/j.egypro.2009.01.181

Foaming in amine-based CO2 capture process: Experiment, modeling and simulation

2009· article· en· W2082018554 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnergy Procedia · 2009
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceAmine gas treatingParametric statisticsAbsorption (acoustics)Aqueous solutionWork (physics)Carbon dioxideComposite materialProcess engineeringMechanical engineeringEngineeringMathematicsChemistryOrganic chemistryEnvironmental engineering

Abstract

fetched live from OpenAlex

This work provides a parametric study on foaming behavior in the carbon dioxide (CO2) absorption process using aqueous monoethanolamine (MEA) solutions. Foaming tendency was experimentally evaluated using the pneumatic method modified from ASTM standard, and reported in terms of foaminess coefficient (Σ). Results show that Σ increases and eventually decreases with MEA concentration and CO2 loading. A higher solution temperature reduces Σ. Most tested degradation products and corrosion inhibitors enhance foam tendency. A foaming model was developed to predict pneumatic steady-state foam heights. It consists of an empirical correlation for foam height prediction and a series of subroutine modules for physical property estimation. The model fits well with the experimental foam data with R2 of 0.88.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.744

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 head0.226
Teacher spread0.217 · 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