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Record W2567236786 · doi:10.1002/cjce.22759

Bubble size and bubble velocity distribution in bubble columns under industrial conditions

2016· article· en· W2567236786 on OpenAlexvenueno aff
Melanie Bothe, M.‐A. Christlieb, Marko Hoffmann, Osmond Tedjasukmana, Frank Michaux, Philipp Rollbusch, Marc Becker, Michael Schlüter

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersBundesministerium für Bildung und Forschung
KeywordsBubbleMechanicsMaterials scienceFlow (mathematics)Bubble pointPhysics

Abstract

fetched live from OpenAlex

Bubble column reactors are widely used in many industrial applications due to their simplicity and safety of operation. Despite these advantages, the design and scale‐up of bubble column reactors is still challenging especially for industrial conditions at elevated pressure and temperature. One reason is the uncertainties concerning the specific interfacial area which is directly dependent on the bubble size distribution, bubble velocity, and gas hold‐up. All these parameters are difficult to measure under industrial conditions due to the opaqueness of the bubbly flow and the safety risks of using organic solvents at elevated pressures and temperatures. This article introduces endoscopic bubble image velocimetry, a new measuring method that enables the detection of bubble sizes and bubble velocities in organic solvents at elevated pressure and temperature (p max = 1.85 MPa and T max = 70 °C) for maximal gas hold‐ups of 16 %. For this system it becomes evident that the bubble size distribution for low superficial gas velocities is almost unaffected by pressure and temperature, whereas the bubble velocity decreases slightly.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.360

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 head0.173
Teacher spread0.165 · 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

Citations24
Published2016
Admission routes1
Has abstractyes

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