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Record W2131029807 · doi:10.1002/aic.10707

Size distributions and stability of toluene diluted heavy oil emulsions

2005· article· en· W2131029807 on OpenAlexaff
Chandra W. Angle, Hassan Hamza, Tadeusz Dąbroś

Bibliographic record

VenueAIChE Journal · 2005
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsBreakageCoalescence (physics)Surface tensionAsphalteneTolueneChemistryOil dropletAdsorptionZeta potentialReynolds numberEmulsionThermodynamicsChemical engineeringTurbulenceMaterials scienceComposite materialNanoparticleNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The sizes and stability of oil droplets created from various concentrations of heavy oil‐in‐toluene at a fixed oil:water ratio were investigated during turbulent flow in model process water. The Reynolds number ( Re ) ranged from 17,000 to 34,500 and was obtained by stirred tank mixing with a Rushton turbine. The droplet sizes were monitored using laser light scattering. Results showed that at high Re and low oil concentrations (that is, low drop‐surface coverage), breakage of the droplets was the dominant process, but as Re was reduced, coalescence was dominant. Droplets were less prone to breakage as the oil concentrations in toluene increased, and droplet sizes approached a steady state quickly during mixing. Their size distributions broadened and stability increased as heavy oil in toluene increased. Stability was attributed to a surface coverage by asphaltenes and the consequent interfacial elasticity that provided resilience to breakage. Equilibrium interfacial tension σ E was determined by fitting a diffusion‐limited kinetic mathematical model to the data. The Gibbs adsorption model gave a monolayer surface coverage of 3 nm 2 /mol asphaltenes, consistent with other published results. High zeta potential of the droplets also hindered coalescence. © 2005 American Institute of Chemical Engineers AIChE J, 2006

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 categoriesInsufficient payload (model declined to judge)
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 score0.094
Threshold uncertainty score1.000

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)0.0010.000

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 head0.012
GPT teacher head0.254
Teacher spread0.243 · 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.

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

Citations24
Published2005
Admission routes1
Has abstractyes

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