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Record W2021967074 · doi:10.1021/ie050570d

Rigorous Model for Predicting the Behavior of CO<sub>2</sub> Absorption into AMP in Packed-Bed Absorption Columns

2005· article· en· W2021967074 on OpenAlexaff
Ahmed Aboudheir, Paitoon Tontiwachwuthikul, Raphael Idem

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2005
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsPacked bedAbsorption (acoustics)Column (typography)Absolute deviationAqueous solutionAnalytical Chemistry (journal)Materials scienceThermodynamicsChemistryChromatographyPhysicsMathematicsPhysical chemistryStatisticsComposite material

Abstract

fetched live from OpenAlex

A rigorous computer model was developed for the simulation of the absorption of CO 2 in aqueous 2-amino-2-methyl-1-propanol (AMP) solutions in a packed absorption column that takes into account the heat effects. This model predicts the concentration and the temperature profiles along the packed column for the CO 2 −AMP system. These profiles were compared with the experimental data that were obtained from two pilot-plant studies. The first study was with a column packed with 12.7-mm Berl Saddles, and the second study was with a high-efficiency structured packed absorber. The predicted results were found to be in close agreement with the measured values. For the experimental data from University of British Columbia, the average absolute deviations between the predicted and measured data in terms of concentration and temperature profiles are 9.7% and 2.3%, respectively. For the experimental data from University of Regina, the average absolute deviation between the predicted and measured concentration profiles is 13.8%. The model predictions could be improved by using more accurate physicochemical properties, when available.

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)0.001
metaresearch head score (Gemma)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.062
GPT teacher head0.308
Teacher spread0.246 · 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".

Quick stats

Citations58
Published2005
Admission routes1
Has abstractyes

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