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Record W2163216023 · doi:10.1109/eicccc.2006.277211

Mass Transfer in a Spray Column for CO2 Removal

2006· article· en· W2163216023 on OpenAlexaff
J.-F. Kuntz, Adisorn Aroonwilas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsMass transferNozzlePacked bedColumn (typography)Volumetric flow rateTrayChromatographyFlue gasMass transfer coefficientAnalytical Chemistry (journal)Carbon dioxideMaterials scienceChemistryMechanical engineeringOrganic chemistryPhysicsMechanicsEngineering

Abstract

fetched live from OpenAlex

The performance of many industrial applications such as gas conditioning and gas scrubbing relies on efficient mass transfer in gas-liquid contacting devices. The most common contacting devices are packed and tray columns. The spray column has been used in applications to remove sulfur dioxide (SO2) from flue gas. There is little published data on the efficiency of a spray column for the removal of carbon dioxide (CO2). In this paper the efficiency of the spray column was experimentally tested for the removal of CO2with monoethanolamine (MEA) as the solvent. The experiments were carried out in a 0.1-m diameter column at varying liquid-to-gas (UG) ratios. The overall rate of mass transfer was measured by collecting and analyzing samples of a simulated flue gas entering and leaving the column. The efficiency of the spray in terms of mass transfer coefficient and the effective mass-transfer area produced were reported as a function of process variables. The process variables include the gas flow rate, liquid flow rate, spray nozzle type and the liquid and gas compositions. The efficiency of the spray column was then directly compared to a packed column with structured packing.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.182
Teacher spread0.177 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

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