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Record W2018431757 · doi:10.1021/ie800601r

Rag Layers in Oil Sand Froths

2008· article· en· W2018431757 on OpenAlexafffund
Mehrrad Saadatmand, Harvey W. Yarranton, Kevin Moran

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2008
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsSyncrude (Canada)University of Calgary
FundersSyncrudeSuncor Energy Incorporated
KeywordsHeptaneCoalescence (physics)SettlingEmulsionOil sandsChemistryDiluentChemical engineeringTolueneMineralogyMaterials scienceChromatographyEnvironmental scienceAsphaltOrganic chemistryComposite materialEnvironmental engineering

Abstract

fetched live from OpenAlex

During the settling stages in some oil sands froth treatments, a rag layer (an undesirable mixture of dispersed oil, water, and solids) can form at the water−oil interface. To investigate rag layer formation, oil sand froths were diluted with mixtures of toluene and heptane and the diluted froths were centrifuged in steps of increasing rpm. The volumes of oil phase, rag layer, free water, and sediment were measured after each step. The data obtained from the experiments were used for material balances to determine the composition of the rag layers. The size and properties of the rag layer solids were also measured. Two mechanisms were found to influence rag layer formation: slow coalescence of emulsified water between 1500 and 3000 rpm (200−1000 times gravity); trapping of fine intermediate to oil wet solids at higher rpm and residence times. The main process factors affecting rag formation appear to be the type of diluent and asphaltene precipitation. As well, higher quality oil sand produced much smaller rag layers.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.087
GPT teacher head0.306
Teacher spread0.219 · 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

Citations28
Published2008
Admission routes2
Has abstractyes

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