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Extraction of Bitumen from Oil Sands Using Deep Eutectic Ionic Liquid Analogues

2015· article· en· W2511426990 on OpenAlexaboutno aff
Nuerxida Pulati, Aron Lupinsky, Bruce G. Miller, Paul C. Painter

Bibliographic record

VenueEnergy & Fuels · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsnot available
Fundersnot available
KeywordsNaphthaEutectic systemAsphaltDeep eutectic solventExtraction (chemistry)Oil sandsChemical engineeringIonic liquidSolventChemistryMixing (physics)Choline chloridePetrochemicalMaterials scienceChromatographyOrganic chemistryComposite materialCatalysis

Abstract

fetched live from OpenAlex

It is demonstrated that bitumen can be separated from “water-wet” Alberta oil sands and “oil-wet” Utah oil sands using a so-called analogue ionic liquid (IL) based on deep eutectic mixtures of choline chloride and urea (ChCl/U) together with a diluent such as naphtha. Unlike conventional ILs, these eutectics are relatively cheap and environmentally friendly. The process is straightforward and involves simply mixing the components at ambient temperatures followed by standard solid/liquid and liquid/liquid separation steps. The ChCl/U mixture appears to reduce the adhesion of bitumen to sand, facilitating separation. It is also immiscible with hydrocarbons such as bitumen or oil. Coupled with a large density difference, this results in a sharp phase separation of hydrocarbons from the ChCl/U mixture. The ChCl/U deep eutectic essentially acts as a separating fluid, keeping the naphtha-diluted bitumen and extracted sand apart, facilitating subsequent separations and solvent recovery steps. However, ChCl/U mixtures are highly viscous at ambient temperatures, but high concentrations of this deep eutectic in water also work well. Initial scale-up work suggests that this approach may form the basis for a viable large-scale process.

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 score0.028
Threshold uncertainty score0.438

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 head0.033
GPT teacher head0.258
Teacher spread0.225 · 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

Citations46
Published2015
Admission routes1
Has abstractyes

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