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Record W2507578302 · doi:10.1002/cjce.22643

Thermal kinetics analysis in microwave‐assisted oil sands separation

2016· article· en· W2507578302 on OpenAlexafffundvenue
Karumudi Rambabu, Natalia Semagina, Carlos F. Lange

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsOil sandsAsphaltThermalDissipationSettlingBuoyancyMaterials scienceMicrowaveThermal conductionPhase (matter)Environmental scienceMechanicsMineralogyComposite materialGeologyChemistryThermodynamicsEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract This computational study addresses oil sands separation under microwave irradiation. Electric field distribution and microwave energy dissipation were analyzed at the level of sand particles and bitumen. The simplified model does not include water or gas present in the oil sands. The microwave power dissipated in bitumen was found to be about 32 times that of the sand particles. In order to investigate the feasibility of heating the bitumen phase preferentially and sustaining a thermal gradient between the bitumen phase and the solid phase, thereby reducing the thermal losses to the solid phase, time scales were estimated using several different scenarios. The small size of the particles in the solid phase caused the time needed to reach thermal equilibrium between phases to be extremely short, in the order of 10 to 100 ms. To achieve separation times shorter than these, pressures up to 100 GPa would have to be sustained across the oil sands layer. Alternatively, buoyancy‐driven separation by settling would require accelerations in the order of 100 000 m/s 2 (10 5 g). Therefore, the heating and separation of bitumen within the thermal conduction time scales seem to be theoretically possible but associated with high technological challenges in their implementation.

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 score0.019
Threshold uncertainty score0.277

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 head0.008
GPT teacher head0.200
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

Citations5
Published2016
Admission routes3
Has abstractyes

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