Novel Polymeric Additives to Improve Oil Sands Tailings Consolidation
Bibliographic record
Abstract
Abstract It is estimated that the Athabasca Oil Sands in Alberta, Canada, contain 1.7 trillion barrels of oil, but producing one barrel of oil from surface-mined oil sands also produces 1.8 metric tons of solid tailings suspended in 2 m3 of water. Traditionally, tailings slurries were discharged into settling ponds, where solids slowly settled over periods of decades or longer. Since Canadian ERCB Directive 74 went into effect on 1 July 2010, however, regulatory pressure to quickly and efficiently separate tailings from the water has mounted. Directive 74 mandates that by 1 July 2012, 50% of tailings solids must be removed from waste streams. Furthermore, the captured solids should be trafficable after five years, defined as possessing a shear strength of at least 10 kPa. The flocculation performance of chemical additives ranging from inorganic salts to high molecular weight organic polymers has been previously assessed, but a procedure for meeting Directive 74 is still uncertain because any proposed solution must deal with a wide range of water quality conditions, mineralogical substrates, and particle sizes. Bench-scale evaluations of novel polymeric additives on Athabasca oil sands were performed in both 1-L graduated cylinders and a thickener. The compactness of captured solids was measured by density and solids content, dewatering was assessed by capillary suction times, and the shear strength of flocculated particles was determined rheologically. The additives were observed to greatly improve flocculation, dewatering, and growth of shear strength relative to conventional polymer treatments.
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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 | 0.000 |
| 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) | 0.002 | 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".