MétaCan
Menu
Back to cohort
Record W4234238970 · doi:10.1021/ef9009586

Recovery of Bitumen from Oil or Tar Sands Using Ionic Liquids

2009· article· en· W4234238970 on OpenAlexaboutno aff
Paul C. Painter, Phillip Williams, Ehren Mannebach

Bibliographic record

VenueEnergy & Fuels · 2009
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsAsphaltExtraction (chemistry)SlurryMixing (physics)Environmental scienceAsphaltenetar (computing)Chemical engineeringMaterials sciencePetroleum engineeringChemistryGeologyEnvironmental engineeringChromatographyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

The extraction and separation of bitumen from oil sands for the purpose of processing fuels is relatively expensive and poses several environmental challenges. Roughly two tons of oil sands are required to produce a barrel of oil, and the separation of the bitumen from sand and clay requires significant amounts of energy and the use of large quantities of water. It is shown here that bitumen in a sample of Canadian tar sands can be recovered using ionic liquids (ILs) and organic solvents. 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 at somewhat elevated (∼55 °C) or ambient temperatures (∼25 °C). Essentially all of the bitumen is released from the sand. 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. Because both the IL and water can be recycled through the system and used repeatedly, this process has the potential for ameliorating many of the environmental problems associated with current extraction methods.

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 score0.025
Threshold uncertainty score0.580

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)0.0010.000

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 head0.018
GPT teacher head0.235
Teacher spread0.216 · 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

Citations110
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicIonic liquids properties and applicationsFrench-language works237,207