Solvent Selection for Asphalt Particles Production of Canadian Oil Sands Bitumen
Bibliographic record
Abstract
Asphaltenes from Canadian oil sands bitumen were obtained by solvent precipitation with two mixed solvent systems,n-pentane+n-hexane mixed solvent and n-pentane+cyclopentane mixed solvent.Several analytical means were conducted to study the variation of asphaltene properties with the solvent changing,such as saturate,aromatic,resin and asphaltene compositions,elemental composition,average molecular structures and aromatic sheets stacking structures.The experiment results indicated that the yield of asphaltene could be remarkable reduced with the increase of n-hexane or cyclopentane proportion in the mixed solvent and the softening point of asphaltene increased correspondingly.When the n-pentane+cyclopentane mixed solvent was used with a small amount of cyclopentane in n-pentane,the softening point of asphaltene could meet the demand for asphalt spray granulation and the n-pentane+cyclopentane mixed solvent was suitable to be the solvent system in the asphalt particles production from Canadian oil sands bitumen.The n-pentane+cyclopentane mixed solvent containing about 10% cyclopentane could decrease the yield of deoiled asphalt by 4 percent and promote the softening point of asphalt by 10-12℃.Hence,nice asphalt particles could be produced,and the n-pentane+cyclopentane mixed solvent with 10% cyclopentane would be the right candidate solvent system for Canadian oil sands bitumen processing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".