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Record W2329140767 · doi:10.1021/ie5017927

Negative Impact of High Stirring Speed in Laboratory-Scale Three-Phase Hydrogenations

2014· article· en· W2329140767 on OpenAlexafffund
Inci Ayranci, Suzanne M. Kresta, Jing Shen, Natalia Semagina

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImpellerMass transferRushton turbineMass transfer coefficientTurbineMechanicsMaterials scienceVolumetric flow rateChemistryKinetic energyThermodynamicsChromatographyPhysics

Abstract

fetched live from OpenAlex

An increase in stirring speed is generally considered to be an a priori means of reducing external mass-transfer limitations in fast three-phase hydrogenations that are performed in a stirred tank. We provide experimental evidence for a 300-mL stirred reactor that, above a certain impeller speed, the efficiency of gas–liquid mass-transfer decreases, resulting in the decreased reaction rate. The phenomenon is attributed to the high degree of gas recirculation with large cavities behind the blades. The recirculation may decrease hydrogen concentration in the remainder of the tank, thus decreasing the concentration gradient that controls mass transfer. The model reaction in this work was 2-methyl-3-butyn-2-ol semihydrogenation with Lindlar catalyst Pd–Pb/CaCO 3 . The test impellers were a Rushton turbine, a down-pumping pitched blade turbine, and up-pumping A340 impellers. The kinetic experiments were combined with the measurement of volumetric gas–liquid mass-transfer coefficient, flow pattern analysis and impeller power demand calculations. Although the study does not include kinetic analysis, it provides guidance to the three-phase reaction system analysis that the highest stirring speed may enhance mass-transfer limitations and should not be used without caution.

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)0.001
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 score0.216
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.035
GPT teacher head0.321
Teacher spread0.286 · 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
Published2014
Admission routes2
Has abstractyes

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