Investigation on alternative disposal methods for froth treatment tailings—part 1, disposal without asphaltene recovery
Bibliographic record
Abstract
Abstract In paraffinic froth treatment, tailings from the tailings solvent recovery unit (TSRU) contain a significant fraction of asphaltenes. In current commercial practice, the TSRU tailings are disposed of in a tailings pond where the large asphaltene particles and coarse tailings settle and the fines form mature fine tailings (MFT). With increasing public concern about the environmental impact of tailings ponds and stricter government regulations on tailings disposal, the oil sand industry has increased efforts to find alternatives that eliminate or reduce the use of tailings ponds. In collaboration with Total E&P Canada (TEPCA), CanmetENERGY conducted an extensive research program to explore alternatives for TSRU tailings disposal. TSRU tailings were produced from TEPCA's froth treatment pilot tests conducted at the CanmetENERGY froth treatment facility. Various processes were investigated, including flocculation and thickening, filtration, and centrifugation. A large number of bench‐scale flocculation‐thickening and filtration tests were conducted, followed by numerous pilot‐scale flocculation‐thickening tests and centrifugation tests. The experimental results demonstrate that TSRU tailings can be flocculated and thickened to produce paste‐like sediment, and the hot water can be recovered and recycled. The thickened TSRU tailings have good water drainage and deposition of the sediment on a beach would result in further dewatering. Centrifugation of the TSRU tailings or filtration of the thickener underflow can produce a cake that appears to be very dry and suitable for disposal without pond containment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".