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Record W2129691001 · doi:10.1002/cjce.20556

Scaling up of silverson rotor–stator mixers

2011· article· en· W2129691001 on OpenAlexvenueno aff
Steven Hall, Mike Cooke, Andrzej W. Pacek, Adam Kowalski, D. Rothman

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilUniversity of ManchesterRoyal Commission for the Exhibition of 1851Royal Society
KeywordsScalingStatorMechanicsSauter mean diameterDrop (telecommunication)Rotor (electric)Coalescence (physics)BreakageMaterials scienceVolumetric flow rateEmulsionThermodynamicsPhysicsEngineeringMechanical engineeringMathematicsComposite materialChemical engineering

Abstract

fetched live from OpenAlex

Abstract In‐line rotor–stator mixers are widely applied in industry for the formulation of emulsion products; however, the current understanding of breakage/coalescence in such devices is limited. In this study the effect of rotor speed, flow rate and dispersed phase viscosity on drop size distributions in an industrial and in a lab scale in‐line Silverson rotor–stator mixer were investigated. Silicone oils with viscosities of 9.4 and 339 mPa s at 1 wt.% were emulsified in surfactant solution. Sauter mean diameters were correlated with various parameters and it has been found that Weber number is the most appropriate scaling parameter. Energy dissipation rate is a marginally poorer scaling parameter and this term requires prior knowledge of the experimentally determined power constants. Based on these observations it seems sensible to use Weber number as the preferred scaling‐up parameter.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.156
Teacher spread0.147 · 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 source (direct Gemma or distilled Codex), 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

Citations56
Published2011
Admission routes1
Has abstractyes

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