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Record W2462993456 · doi:10.1002/cjce.22564

Impact of fine solids on mined athabasca oil sands extraction II. Effect of fine solids with different surface wettability on bitumen recovery

2016· article· en· W2462993456 on OpenAlexaffvenue
Zoe A. Zhou, Haihong Li, Ross Chow, O. B. Adeyinka, Zhenghe Xu, Jacob H. Masliyah

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversity of AlbertaImperial Oil (Canada)Alberta Innovates
Fundersnot available
KeywordsSlurryOil sandsAsphaltWettingExtraction (chemistry)Total dissolved solidsSuspended solidsParticle sizeKaoliniteChemistryMaterials scienceChemical engineeringMineralogyWaste managementChromatographyEnvironmental engineeringComposite materialWastewaterEnvironmental science

Abstract

fetched live from OpenAlex

ABSTRACT The effect of solids wettability on bitumen extraction recovery and froth quality was examined by adding fine solids (< 5 and < 40 μm silica, or < 5 μm kaolinite clays) and surfactants (dodecylamine) to the oil sands slurry. Oil sands extraction tests were run using a 1‐L Denver flotation cell at 50 °C. The results show that solids wettability plays a significant role in bitumen extraction recovery and froth quality. Adding fine solids to the oil sands slurry decreased bitumen recovery, and increased the amount of solids and water in the bitumen froth. The addition of finer solids (< 5 μm) into the oil sands slurry resulted in more solids and water in the bitumen froth than the addition of coarser solids (< 40 μm). Compared to hydrophilic solids, hydrophobized fine solids due to amine addition led to more fine solids being recovered to the bitumen froth. However, recovery of hydrophobized fine solids (resulting from amine adsorption) increased with smaller particle sizes, which is contrary to the trend in single mineral flotation where hydrophobized solids recovery increased with increasing particle sizes (for the size faction of < 44 μm).

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 score0.012
Threshold uncertainty score0.521

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 head0.006
GPT teacher head0.225
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 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

Citations10
Published2016
Admission routes2
Has abstractyes

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