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Record W2317263097 · doi:10.1021/ef1012819

Improving Oil Sands Processability Using a Temperature-Sensitive Polymer

2011· article· en· W2317263097 on OpenAlexaff
Jun Long, Hongjun Li, Zhenghe Xu, Jacob H. Masliyah

Bibliographic record

VenueEnergy & Fuels · 2011
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAsphaltOil sandsTailingsSettlingSlurryPolymerAsphalteneExtraction (chemistry)Materials scienceComposite materialChemistryMetallurgyChromatographyEnvironmental scienceEnvironmental engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

A temperature-sensitive polymer, poly(N-isopropylacrylamide), was tested as a process aid to process a low-grade, high-fines oil sand ore. Two sets of bitumen extraction tests were carried out. In test set I, both oil sands slurry conditioning and bitumen flotation were conducted at 23 °C. In test set II, the slurry was conditioned at 23 °C, and, however, the bitumen flotation step was carried out at 40 °C. It was found that the use of the polymer in test set I imposed a negative impact on both bitumen recovery and bitumen froth quality. In test set II, the addition of the polymer at the bitumen extraction step improved bitumen recovery and significantly accelerated solids settling of the tailings but deteriorated the bitumen froth quality. The improvement in bitumen recovery and tailings settling at the higher operating temperature was attributed to the change of the polymer from a long, extended structure to a coiled configuration, resulting in the formation of compacted floccules of fine solids. Atomic force microscopy (AFM) was used to directly measure the long-range interaction and adhesion forces of fine solids-bitumen in industrial process water. Results from the AFM force measurements indicated potential slime coating of fine solids on bitumen, thus providing a scientific basis on the reduced bitumen froth quality with the polymer addition.

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 score0.045
Threshold uncertainty score0.940

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 head0.012
GPT teacher head0.208
Teacher spread0.197 · 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

Citations30
Published2011
Admission routes1
Has abstractyes

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