MétaCan
Menu
Back to cohort
Record W2892430540 · doi:10.1002/aic.16424

Impact of a novel bitumen extraction process on mined oil sands tailings behavior

2018· article· en· W2892430540 on OpenAlexafffundabout
Feng Lin, Yuming Xu

Bibliographic record

VenueAIChE Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsNatural Resources Canada
FundersNatural Resources CanadaAlberta Innovates
KeywordsTailingsOil sandsAsphaltDewateringConsolidation (business)Geotechnical engineeringSettlingExtraction (chemistry)Petroleum engineeringCentrifugeWaste managementEnvironmental scienceGeologyEngineeringMaterials scienceChemistryEnvironmental engineeringChromatographyMetallurgyComposite material

Abstract

fetched live from OpenAlex

Hybrid bitumen extraction (HBE) at ambient conditions has potential, as an alternative to the commercially used modified Clark hot water extraction (CHWE) for mined oil sands. In this work, the dewatering behaviors of tailings suspensions generated on a pilot scale from standard CHWE and from HBE are studied. Using a centrifuge‐based technique, sediment height at a given centrifugal acceleration is measured as a function of time, as is equilibrium consolidation height vs. acceleration. By fitting the measurements to a consolidation model, compressive yield stress data is obtained. Settling of tailings suspensions under earth's gravitation are also observed for comparison. Results demonstrate that the suspensions from the pilot HBE process settle faster than those from standard aqueous process at a given acceleration. The data also suggest that the suspensions from the pilot HBE are more compressible, as indicated by higher final compressed solids weight fraction for a given yield stress.© 2018 Her Majesty the Queen in Right of CanadaAIChE Journal © 2018 American Institute of Chemical EngineersReproduced with the permission of the Minister of Natural Resources Canada. AIChE J , 65: 250–258, 2019

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 score0.033
Threshold uncertainty score0.487

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 head0.016
GPT teacher head0.330
Teacher spread0.314 · 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

Citations7
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueAIChE JournalSame topicEnhanced Oil Recovery TechniquesFrench-language works237,207