Role of binary solvent and ionic liquid in bitumen recovery from oil sands
Bibliographic record
Abstract
A binary solvent of ethyl acetate and n‐heptane was made and applied together with ionic liquids (ILs, 1‐ethyl‐3‐methyl imidazolium tetrafluoroborate ([Emim]BF4)) to extract bitumen from oil sands at ambient conditions. Results of bottle tests show that the bitumen recovery is highly dependent on the volume ratio of ethyl acetate to n‐heptane. The maximum recovery was obtained at the ethyl acetate‐to‐n‐heptane ratio of 3:6. With external addition of ILs, an additional improvement of ∼10 % of bitumen recovery was observed (from 83 to 93 % at the ethyl acetate‐to‐n‐heptane ratio of 3:6). Based on the intensive investigation of key operational parameters (i.e. ILs‐to‐solvent ratio, agitation time, and conditioning time), a set of recommended extraction conditions were proposed to maximize the bitumen recovery. Further fractionation of the extracted bitumen together with FTIR and SEM detection on the residual solids indicated that ILs addition could increase the dissolution of bitumen fractions into solvents, while reducing the entrapment of fine particles in the solvents. The improvement of bitumen recovery by ILs addition was attributed to the enhancement of the liberation of bitumen components from mineral solids surfaces during oil sands solvent extraction. However, the asphaltenes were observed to be rejected during processing due to their accumulation at the oil‐ILs interface as a film.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".