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Record W2058336734 · doi:10.1002/cjce.5450790322

Enhancement of the overall volumetric oxygen transfer coefficient in a stirred tank bioreactor using ethanol

2001· article· en· W2058336734 on OpenAlexafffundvenue
Yonghong Bi, Gordon A. Hill, Robert J. Sumner

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2001
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImpellerDistilled waterMass transfer coefficientMass transferBioreactorBubbleOxygenEthanolMaterials scienceChromatographyChemistryContinuous stirred-tank reactorAnalytical Chemistry (journal)ThermodynamicsMechanics

Abstract

fetched live from OpenAlex

Abstract Ethanol was observed to improve the oxygen mass transfer rate into a well‐mixed bioreactor. The effects of impeller speed and ethanol concentration on the oxygen transfer from air to the water phase and on the average bubble diameter in a stirred tank bioreactor are reported and modelled. The results show that the oxygen mass transfer coefficient ( kLa ) increases from 0.002 to 0.017 s −1 (for distilled water) due to the increase of impeller speed from 135 to 600 rpm. With increasing ethanol concentration from 0 to 8 g/L, the oxygen mass transfer coefficients increase from 0.015 to 0.049 s −1 and from 0.017 to 0.076 s −1 , for impeller speeds of 450 and 600 rpm, respectively. The average bubble diameter decreased from 7.0 mm to 1.7 mm in pure distilled water as the impeller speed was increased from 135 to 600 rpm. When ethanol was present in the aqueous phase, the bubble diameter fell from 6.0 mm to 0.6 mm as the impeller speed was similarly increased.

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 score0.404
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.182
Teacher spread0.172 · 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

Citations8
Published2001
Admission routes3
Has abstractyes

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