Investigation on alternative disposal methods for froth treatment tailings—part 2, Recovery of asphaltenes
Bibliographic record
Abstract
Abstract In collaboration with Total E&P Canada (TEPCA), CanmetENERGY conducted an extensive research program to investigate possible alternatives for TSRU tailings disposal. We have reported on alternative methods for TSRU tailings disposal without recovery of asphaltenes in an earlier publication.[1] Because the asphaltenes are high‐molecular‐weight hydrocarbons and may have potential for use as fuel or paving material, in this work, we investigate possible approaches for recovery of the asphaltenes from TSRU tailings. Two methods were tested, solvent extraction and aggregation. In the first method aromatic solvent is mixed with TSRU tailings to dissolve the asphaltenes followed by centrifugation to remove mineral solids and water. The experimental results demonstrated that more than 90% of the asphaltenes in the tailings can be recovered from the tailings. The recovered asphaltenes contained only a small fraction of mineral solids and may be useable as coker feed. In the second method, TSRU tailings are agitated at elevated temperature (80°C) at which the asphaltene particles form large aggregates and separate from the tailings, giving an asphaltene‐rich phase and almost asphaltene‐free tailings. The recovered asphaltene aggregates still contain significant amounts of mineral solids and water, and would require further treatment. Several coking tests were conducted using the recovered asphaltenes and the asphaltene aggregates. The results demonstrated that about 40 wt% of the recovered asphaltenes can be converted to lighter oil fractions under the coking conditions used in this work.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".