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Record W4385238932 · doi:10.1021/acs.iecr.3c01600

Mass Transfer Performance and Hydrodynamics of a Bubble Column Reactor at Offshore Floating Conditions

2023· article· en· W4385238932 on OpenAlexaff
Nasim Heydari, Faı̈çal Larachi, Ragna Kipping, Markus Schubert

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBuoyancyBubbleMechanicsMass transferOscillation (cell signaling)Reynolds numberCoalescence (physics)Added massAmplitudeChemistryVibrationPhysicsTurbulenceAcousticsOptics

Abstract

fetched live from OpenAlex

Hydrodynamics and mass transfer characteristics are analyzed in a bubble column subjected to simulated ship motions using a hexapod robot with six-degree-of-freedom motions. Wire-mesh sensors have been used for collecting local gas holdup and flow patterns under nonreactive conditions. Additionally, the electrical conductivity of the liquid phase during CO 2 uptake was extracted to determine hydroxide ion consumption rates as an indicator of mass transfer. The two-phase flow patterns in the bubble column operating under offshore conditions deviate significantly from the stationary ones due to the buoyancy-driven lateral migration of bubbles. The consumption rates of hydroxide ions during the chemical absorption of CO 2 revealed that the amplitude of oscillations imposed on the bubble column is the dominant factor for the mass transfer in moving columns. Contrarily, the effect of the oscillation frequency is negligible, which is attributed to bubble coalescence and bubble flow maldistribution in the bubble column subjected to rotational oscillations. Bubble-free zones are formed in the liquid phase because of the buoyancy effects in the column tilted from the vertical axis, while the frequency of the oscillations does not add any additional effects to the bubble kinematics. The latter is attributed to the low shear rates maintained over the range of frequencies simulating marine swells.

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 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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.270
Teacher spread0.226 · 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 teacher head, 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

Citations4
Published2023
Admission routes1
Has abstractyes

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