Flocculation Efficiency and Spatial Distribution of Water in Oil Sands Tailings Flocculated with a Partially Hydrophobic Graft Copolymer
Bibliographic record
Abstract
This work investigates the effect of partially hydrophobic grafted polymers on flocculation and dewatering of oil sands mature fine tailings. Here, we combine confocal microscopy and rheology to investigate how the graft density of ethylene-propylene-diene grafted with hydrolyzed poly(methyl acrylate) (EPDM- g -HPMA) affects its dispersion in water and flocculation efficiency in terms of sediment solids content and long-term dewatering of oil sands tailings. Increasing the graft density from 30 to 50% makes the flocculant easier to disperse, increases the rate of initial dewatering, and also enhances the viscoelastic response of the flocculated sediments. Conversely, the long-term rheological properties of the flocculated sediments were similar for all flocculants. Tri-dimensional microscopic details of the spatial distribution of water within the flocculated sludge provide novel insights into the performance of the flocculants. Increasing the graft density in EPDM- g -HPMA traps more water within the individual flocs and, consequently, decreases the post-flocculation dewatering rate. Our systematic approach confirms the importance of the spatial distribution of water in the flocculated sediment, which depends on how the flocculant is dispersed and how it retains water in the flocs.
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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) | 0.000 | 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".