MétaCan
Menu
Back to cohort
Record W2001810959 · doi:10.1021/ef0496855

On the Role of Temperature in Oil Sands Processing

2005· article· en· W2001810959 on OpenAlexaff
Jun Long, Zhenghe Xu, Jacob H. Masliyah

Bibliographic record

VenueEnergy & Fuels · 2005
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAsphaltOil sandsAdhesionAerationCoatingViscosityMaterials scienceComposite materialChemistryChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Bitumen recovery from oil sands was found to severely deteriorate at operating temperatures lower than a “critical” value, suggesting a substantial change in one or more key process variables. A sharp increase in bitumen viscosity at lower temperatures has been considered as the major contributor to such deterioration. On the basis of the fact that the addition of selected chemicals does improve bitumen recovery without affecting bitumen viscosity, there must be other physicochemical factors that affect bitumen recovery and undergo a sharp change with temperature. In the present study, the interaction and adhesion forces between bitumen and sand grains or clays in water were measured as a function of temperature using an atomic force microscope. The results show that the measured adhesion force between clay and bitumen decreases with increasing temperature until a critical value of about 32−35 °C, above which the adhesion force disappears. As the adhesion force between clay and bitumen controls clay slime coating on bitumen surface and subsequently bitumen aeration, increase in the adhesion force with decreasing processing temperature would lead to slime coating and lower bitumen recovery. The effect of a chemical additive, methylisobutyl carbinol (MIBC), on the colloidal forces was also studied. The results show that MIBC addition can reduce the adhesion force between clay and bitumen, thus facilitating bitumen aeration.

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 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 score0.352
Threshold uncertainty score0.281

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)0.0000.000

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 head0.003
GPT teacher head0.194
Teacher spread0.191 · 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.

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

Citations50
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicEnhanced Oil Recovery TechniquesFrench-language works237,207