Evaluation of candidate polymers to maximize geotechnical performance of oil sands tailings
Bibliographic record
Abstract
Oil sands fluid fine tailings deposits are challenging to reclaim due to their inherently high natural water content, low permeability, and low strength. Combinations of polymers and (or) coagulants are used by operators to improve the dewatering and strength properties of the tailings. However, considerably more work has been done to evaluate polymer performance with short-term dewatering metrics rather than with long-term metrics such as consolidation properties. This paper evaluates the potential of four novel polymers for use in fluid fine tailings treatment compared to a commercially available polymer and untreated tailings specimen. The performance of the polymers was assessed through initial screening with respect to short-term dewatering, evaluation of the consolidation and strength properties using large-strain consolidation tests, shear sensitivity in pipeline transport, and finally, large-strain consolidation modelling to appraise the relative potential performance under different strategies, such as terrestrial or aquatic reclamation options. One polymer exhibits remarkably fast dewatering at high void ratios, while another demonstrates dense and shear-resistant flocs. The paper discusses each polymer’s distinctive tailings fabrics and how their unique merits and limitations would benefit different reclamation eventualities. Finally, potential improvements of the polymers are suggested for future assessment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".