Effects of Atmospheric Drying and Consolidation of Flocculated Thickened Tailings and Centrifuged Cake on Near-Surface Shear Strength
Bibliographic record
Abstract
Concern about the growing volume of fluid fine tailings and difficulties for the oil sands mining companies in meeting the AER’s directive 074 resulted in a new policy document of the Alberta Government (2015) known as the tailings management framework (TMF). The TMF sets out limits on the amount of fluid tailings that can be accumulated at each mine and requires tailings to be progressively treated and reclaimed throughout the project life-cycle and ready-to-reclaim within 10 years of the end-of-mine-life of that project. To meet the regulatory requirements some companies are adding flocculants to the fluid fine tailings, using thickeners or centrifuges to increase the solids content and then using freeze/thaw and drying processes to further dewater and drying the tailings. The effect of flocculating, thickening, centrifugation, and atmospheric drying were investigated by performing large strain consolidation tests, atmospheric evaporation tests, and shear strength tests on treated fluid fine tailings. The atmospheric drying test results showed that the centrifuge cake gained shear strength much faster than the flocculated and thickened tailings with drying time. Results also show that the atmospheric drying treatment has a much larger shear strength gain than the large strain consolidation treatment for fluid fine tailings. Atmospheric drying can produce a product with a trafficable surface, but the tailings had to be dried to an unsaturated state. The effects and evaluations of the treatments are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".