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Record W2057530735 · doi:10.1021/ef100765u

Recovery of Bitumen from Utah Tar Sands Using Ionic Liquids

2010· article· en· W2057530735 on OpenAlexaboutno aff
Paul C. Painter, Phillip Williams, Aron Lupinsky

Bibliographic record

VenueEnergy & Fuels · 2010
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsAsphaltSlurryMixing (physics)AsphalteneToluenetar (computing)Ionic liquidSolventChemical engineeringViscosityEnvironmental scienceGeologyMineralogyChemistryMaterials scienceOrganic chemistryEnvironmental engineeringComposite materialCatalysis

Abstract

fetched live from OpenAlex

Hot or warm water processes are used to extract bitumen from Canadian oil or tar sands. The application of these methods to the processing of tar sand deposits found in the Western United States, notably Utah, has not been commercially successful, however, because of the consolidated nature of the deposits and the high viscosity of the bitumen. It is demonstrated here that a previously developed method employing ionic liquids (ILs) together with a nonpolar solvent such as toluene can effect a separation at ambient temperatures (∼25 °C), although with greater difficulty than Canadian oil sands. Essentially, a multiphase system consisting of a sand and clay slurry, an ionic liquid layer, and an organic layer containing the bitumen can be formed by simply mixing the components. More than 90% of the bitumen is released from the sand, but only in successive extractions. Water is not used in this stage of the separation, but relatively small amounts are used to separate entrained IL from the sand and clays.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations167
Published2010
Admission routes1
Has abstractyes

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