Effect of flyash addition to flocculation and freezing and thawing treatment on consolidation of oil sands fluid fine tailings
Bibliographic record
Abstract
The total volume of fluid fine tailings (FFT), reached 1,270 Mm3 in 2019. Extensive research is underway by a number of operators to develop dewatering technologies for oil sand tailings reclamation to comply with Directive 085 issued by the Alberta Energy Regulator. A promising technology for the disposal of FFT is to add flocculents and then use thickeners or centrifuges to decrease the water content. Following this treatment, freezing/thawing processes can then be utilized to further dewater the tailings. The effect of flocculation/flyash addition and thickening coupled with freezing/thawing treatments on FFT was investigated by performing large-strain consolidation and shear strength tests on the treated flocculated TTs. It was found that flocculation and thickening treatment increases the hydraulic conductivity of the treated TT which will result in the TT consolidating much faster than the untreated TT. The most important benefit of the flyash addition is the increase in shear strength and hydraulic conductivity of the flyash-treated TTs. The benefit of the freezing/thawing treatment processes coupled with flyash treatment is the increase in the compressibility and hydraulic conductivity at effective stresses lower than 100 kPa and void ratios greater than 1.2, respectively. This will facilitate earlier progressive reclamation required to support hydraulic sand capping.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".