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Record W2103543499 · doi:10.1002/cjce.20282

Bubble shape, gas flow and gas–liquid mass transfer in pulp fibre suspensions

2010· article· en· W2103543499 on OpenAlexaffvenue
Linda Kate Ishkintana, Chad P. J. Bennington

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBubbleMass transferHardwoodVolumetric flow rateSuspension (topology)SoftwoodKraft paperPulp (tooth)Mass transfer coefficientSuperficial velocityChemistryAnalytical Chemistry (journal)ChromatographyKraft processMaterials scienceMechanicsComposite materialFlow (mathematics)

Abstract

fetched live from OpenAlex

Abstract Gas–liquid mass transfer in pulp fibre suspensions in a batch‐operated bubble column is explained by observations of bubble size and shape made in a 2D column. Two pulp fibre suspensions (hardwood and softwood kraft) were studied over a range of suspension mass concentrations and gas flow rates. For a given gas flow rate, bubble size was found to increase as suspension concentration increased, moving from smaller spherical/elliptical bubbles to larger spherical‐capped/dimpled‐elliptical bubbles. At relatively low mass concentrations ( C m = 2–3% for the softwood and C m ≅ 7% for the hardwood pulp) distinct bubbles were no longer observed in the suspension. Instead, a network of channels formed through which gas flowed. In the bubble column, the volumetric gas–liquid mass transfer rate, k L a , decreased with increasing suspension concentration. From the 2D studies, this occurred as bubble size and rise velocity increased, which would decrease overall bubble surface area and gas holdup in the column. A minimum in k L a occurred between C m = 2% and 4% which depended on pulp type and was reached near the mass concentration where the flow channels first formed.

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 score0.837
Threshold uncertainty score0.485

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 integrity0.0000.001
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 head0.004
GPT teacher head0.158
Teacher spread0.155 · 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

Citations5
Published2010
Admission routes2
Has abstractyes

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