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Record W3110146608 · doi:10.1002/cjce.23964

Composition dependence of cyclohexane‐extracted gangue drying at ambient conditions

2020· article· en· W3110146608 on OpenAlexafffundvenue
Reza Khalkhali, Phillip Choi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCyclohexaneAsphaltEvaporationChemical engineeringChemistryGangueDissolutionSolventFlux (metallurgy)Materials scienceChromatographyMineralogyAnalytical Chemistry (journal)Composite materialOrganic chemistryMetallurgyThermodynamics

Abstract

fetched live from OpenAlex

Abstract The drying of residue cyclohexane in a series of reconstituted gangue samples at ambient conditions was studied. The reconstituted gangue samples contained controlled amounts of residual bitumen, water, and fines (solid particles <45 μm). Regardless of the gangue composition, the initial drying flux was constant, and more than 90% of the residual cyclohexane was removed in this stage. The initial drying was driven by the liquid film flow induced by the differences in liquid film's radii of curvature along the pore channels. The liquid was a diluted bitumen solution that was transported to the surface, where cyclohexane evaporated with a rate comparable to that of pure cyclohexane evaporation. Upon the solvent evaporation, the dissolved bitumen was deposited on the sample surface. The residual bitumen content exerted an adverse effect on the initial drying flux because the dissolved bitumen significantly increased the solution viscosity, thereby reducing the liquid film flow rate. There existed an optimum water content range (3.9‐6.0 wt%) that facilitated the formation of bitumen solution film in the pores, favouring drying. When there was a high concentration of water or no water, the initial drying flux was decreased remarkably. The initial drying flux was insensitive to the fines content. Adding fines may have two counteracting effects on the drying process. Fines decreased the mean pore size, thereby lowering the liquid film flow rate. However, the hydrophobicity of the pore surfaces was increased as fines are usually covered by asphaltenes. This favours liquid film formation.

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 score0.008

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.008
GPT teacher head0.194
Teacher spread0.185 · 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

Citations2
Published2020
Admission routes3
Has abstractyes

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