The solvent extraction is a potential choice to recover asphalt from unconventional oil ores
Bibliographic record
Abstract
The abundant unconventional oil ores (about 70% of total world oil) are playing an increasingly important role in global energy supply. To obtain asphalt in unconventional oil ores, different methods, including hot water-based extraction, pyrolysis and solvent extraction, were used to recover asphalt from oil ores (i.e., Canadian, Indonesian, and Iranian oil ores). It is found that asphalt recovery obtained by solvent extraction is the highest. Multi-staged single solvent extraction was used to recover asphalt from oil ores (i.e., toluene, tetrahydrofuran: THF, xylene, petroleum: PE, ethanol), resulting in a cumulative asphalt recovery over 98% at ambient conditions by using toluene. Take Iranian oil ores with the highest oil content (83.79 wt%) as an example, great asphalt recovery was obtained by using multi-staged composite solvents extraction (i.e., [email protected], [email protected], [email protected], [email protected]). It is also found that introduction of toluene in the composite solvents can significantly increase the ability of single solvent’s (xylene, THF, PE and ethanol) asphalt recovery. After solvent extraction, the solvent recovery was more than 95%. This finds suggest that solvent extraction method would be potential choice to recover asphalt from unconventional oil ores, and it possesses great prospect of industrial application in the future.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".