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Record W2149004463 · doi:10.1002/cjce.5450780309

Gas‐liquid mass transfer in low‐ and medium‐consistency pulp suspensions

2000· article· en· W2149004463 on OpenAlexaffvenue
V. B. Rewatkar, Chad P. J. Bennington

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2000
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsCatalyst Paper (Canada)University of British Columbia
Fundersnot available
KeywordsMass transferMass transfer coefficientDistilled waterAnalytical Chemistry (journal)Mass fractionChemistrySuspension (topology)Materials scienceChromatographyComposite material

Abstract

fetched live from OpenAlex

Abstract The volumetric gas—liquid mass transfer coefficient ( k L a ) was measured for low‐ and medium‐consistency pulp suspensions using the cobalt‐catalyzed sulfite oxidation technique. Mass transfer rates were measured in a high‐shear mixer for a range of operating parameters, including the rotor speed ( N = 10 to 50 rev/s), gas void fraction ( X g = 0.10 to 0.40) and fibre mass concentration ( C m = 0.0 to 0.10). k L a measurements were compared with the macroscale flow regime in the vessel (characterized using photographic techniques) and correlated with energy dissipation, gas void fraction and suspension mass concentration in the mixer. We found that gas‐liquid mass transfer was significantly reduced in pulp suspensions, even for low suspension concentrations. Part of this reduction was associated with dissolved components leached from the fibres into the liquid phase. This could account for reductions in k L a of up to 30% when compared with distilled water. The fibres further reduced k L a , with the magnitude of the decrease depending on the fibre mass concentration. Correlations were developed for k L a and compared with results available in the literature.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.427

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 head0.004
GPT teacher head0.157
Teacher spread0.153 · 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 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

Citations11
Published2000
Admission routes2
Has abstractyes

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