Polymeric flocculation of oil sands tailings: Unveiling rheological indicators and dynamics of bitumen release
Bibliographic record
Abstract
Abstract Polymeric flocculation is widely used to accelerate the dewatering and consolidation of oil sands fluid fine tailings (FFTs). Optimizing flocculation requires a fundamental understanding of the changes to the internal structure of the material with polymer addition. Key challenges include sensitivity of flocculation to polymer dose, mixing conditions, and composition of individual FFTs. Moreover, despite the environmental implications, little is known of the effects of flocculation on the mobility of the residual organics present in the tailings. In this paper, advanced rheological tests are used to probe the formation and development of the polymer–clay structure during flocculation in near‐real time. This is achieved through a novel setup for controlled delivery of the polymer directly into the measuring cell of a rheometer. This enables continuous monitoring of the rheological parameters during the flocculation process and yields consistent and reproducible samples. An optical monitoring system is used to relate rheological measurements to water release and to changes in the surface accumulation of the residual bitumen. The water‐release polymer dose is found to be associated with a distinct rheological response highlighting the potential use of rheometry for polymer dosing/mixing optimization in real time and on a continuous basis in thickeners and inline flocculation systems.
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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".