Impact of fugitive bitumen on polymer‐based flocculation of mature fine tailings
Bibliographic record
Abstract
Abstract In bitumen recovery from oil sands, a percentage of the bitumen is lost to tailings. The effect of fugitive bitumen on fines settling and consolidation in tailings ponds remains controversial. In the current study, the settling performance of mature fine tailings (MFT) in response to flocculant addition was considered by studying MFT of varying bitumen content. Bitumen content in the MFT was adjusted by controlled removal of bitumen using a Denver flotation cell. The initial settling rate of flocculated MFT was observed to increase with decreasing bitumen content from 0.45 to 0.18 wt%. A further reduction in bitumen content was found to dramatically decrease the settling rate of flocculated MFT. Such behaviour seems counterintuitive since the polymer flocculant was found to have a greater affinity for ‘clean’ surfaces (Al2O3) than for bitumen contaminated surfaces, as measured by quartz crystal microbalance with dissipation (QCM‐D), which would predict a further increase in settling rate of flocculated MFT with decreasing bitumen content. The reduction in settling rate below a critical bitumen content is thought to result from selective removal of hydrophobic solids, since washing of untreated MFT with toluene is shown to significantly improve settling of flocculated solids. The current study confirms the use of flotation as a viable option to control MFT bitumen content and improve the settling rate of flocculated MFT.
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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) | 0.000 | 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) | 0.001 | 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".