Water soluble polymeric nanofibres for rapid flocculation and enhanced dewatering of mature fine tailings
Bibliographic record
Abstract
Abstract Water‐soluble polymer flocculants have been used to efficiently release entrapped water in oil sands tailings by bridging fine particles to create large heavier flocs which can then settle faster and release water more efficiently. Due to their initial interaction with the fine particles suspended in tailings, polymer nanofibres may perform better than their parent polymers because of the entire surface of the nanofibres being fully accessible to the fine particles. In this work, commercially available poly(acrylamide‐co‐diallyl dimethylammonium chloride) was chosen as a basis for this study. Initial settling rate, supernatant turbidity, water recovery, capillary suction time, and solids content were measured to determine the effect of polymer nanofibres on solid‐liquid separation. The solid forms of the polymer (either as nanofibre or powder) perform better than the polymer solution in each test, with optimum dosages of 5 wt% mature fine tailings (MFT) loading. Nanofibres could achieve settling rates of 60 m/h, while the other forms were only able to achieve 42 m/h. Additionally, the turbidity of the supernatant obtained after flocculation with nanofibres was 15 nephelometric turbidity units (NTU), while the polymer solution and powder produced turbidites of 162 NTU and 70 NTU, respectively. In addition, polymer nanofibres and powders generated larger flocs compared to the polymer solution, which produced small, homogenized flocs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".