Controlling polymer addition flowrate to improve the flocculation of kaolinite suspensions as models for oil sands mature fine tailings
Bibliographic record
Abstract
Abstract Oil sands exploration generates large volumes of mature fine tailings—mixtures of clays, water, and residual bitumen—that remain stable even after settling in tailings ponds for many years. Commonly used flocculants, such as neutral and anionic polyacrylamides, destabilize the colloidal interactions between the clay particles, but unfortunately make flocs that break under shear and retain water. We grafted poly[(vinylbenzyl)trimethyl ammonium chloride] cationic chains onto amylopectin backbones to make a cationic flocculant with controlled hydrophobicity and combined it with a commercial ultra‐high molecular weight anionic polyacrylamide to investigate how the addition flowrates and dosages of both polymers affected the flocculation of kaolinite suspensions. The addition flowrate of the cationic graft polymer changed the capillary suction time of the sediments and turbidity of the supernatant, while the addition flowrate of the anionic polymer affected the solids content of the sediments. The best conditions to capture fines and dewater the sediments were low dosages of anionic polymer and high dosages of cationic polymer. Focused beam reflectance measurements confirmed that the addition flowrate of each polymer affected the capture of clay particles, floc size, and floc shear resistance. A minimum addition flowrate of the cationic polymer was needed to destabilize the suspension and form primary flocs, which could then grow through combined charge neutralization and bridging with the anionic polymer to form shear‐resistant and large flocs in the range from 500 to 1000 μm.
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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) | 0.001 | 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 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".