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Record W2008191683 · doi:10.1002/cjce.21876

Evaluation of breakage kernels for liquid–liquid systems: Solution of the population balance equation by the least‐squares method

2013· article· en· W2008191683 on OpenAlexvenueno aff
Jannike Solsvik, Zsolt Borka, Per Julian Becker, Nida Sheibat‐Othman, Hugo A. Jakobsen

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsBreakagePopulation balance equationPopulationBinary numberDistribution functionFunction (biology)MathematicsLeast-squares function approximationStatisticsApplied mathematicsMechanicsThermodynamicsMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Abstract The breakage frequency and daughter size distribution functions by Coulaloglou and Tavlarides[1] are frequently adopted closures in population balance (PB) modelling. A survey of the extensions and modifications of the Coulaloglou and Tavlarides[1] breakage frequency function is provided. Furthermore, the daughter size distribution functions within the statistical category, herein the model proposed by Coulaloglou and Tavlarides[1], are outlined. Most of the breakage models available in literature commonly assume binary breakage only. Thus, the daughter size distribution function suggested by Diemer and Olson[2] is of interest as higher order breakage can be modelled. The breakage closures are evaluated solving the population balance equation (PBE) for a liquid–liquid emulsification system in a stirred tank. The results obtained from a least‐squares solver are compared with the experimental data when available.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.225
Teacher spread0.209 · 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 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

Citations9
Published2013
Admission routes1
Has abstractyes

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