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Record W4302293055 · doi:10.1002/cjce.24706

Effect of operating factors on liquid–liquid mass transfer and dispersion pattern of sedimentary liquid in a mechanically stirred vessel

2022· article· en· W4302293055 on OpenAlexvenueno aff
Koudai Sunami, Shuhei Horiuchi, Noriko Nishimura, Md. Azhar Uddin, Yoshiei Kato

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsImpellerBaffleMass transferDispersion (optics)Liquid liquidRotational speedRotation (mathematics)Materials scienceMechanicsChemistryChromatographyThermodynamicsOpticsGeometryMechanical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract Liquid–liquid dispersion and mass transfer were investigated in mechanically stirred vessels without baffles by changing operation factors such as an impeller rotation speed, off‐bottom clearance, volumetric liquid ratio, etc. The dispersion regime was categorized into five groups: the sedimentary liquid was kept at the vessel bottom (I), partially elevated without any collision (II), partially dispersed by colliding with the impeller bottom (III), both liquids were partially dispersed by collisions with impeller blades (III’), and the sedimentary liquid was completely dispersed (IV). The dispersion switched to I → II → III → IV with the increasing rotation speed and decreasing off‐bottom clearance. The liquid–liquid mass transfer rate was significantly enhanced with the collision of the sedimentary liquid with the impeller bottom, and subsequently increased with the increasing rotation speed, volumetric liquid ratio, and vessel diameter and with the decreasing off‐bottom clearance. A multiple regression analysis method was applied to determine the mass transfer rates of III and III’.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.515

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 head0.003
GPT teacher head0.169
Teacher spread0.166 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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