Rapid tailings dewatering by flocculation-sedimentation-filtration-microwave sequential treatment
Bibliographic record
Abstract
Achieving low-water-content oil sands fluid fine tailings (FFT) for reclamation is a significant challenge, even with the otherwise effective dewatering methods such as pressure filtration. FFT from mineable oil sands processing comprises high concentrations of clays,which impede its dewatering. This study established a classification of the dominant forms of water in oil sands tailings, namely free water (FW), interstitial pore water (IPW), vicinal pore water (VPW), and surface-bound water (SBW), based on the ease of their removal in dewatering. Most FW and IPW could be removed by sedimentation and filtration following chemical treatment with polymeric flocculants and coagulants. However, removing the VPW and SBW by mechanical dewatering methods was very challenging. Microwave heating was found to be an effective alternative to remove part of the VPW and SBW from filtered FFT or to facilitate subsequent water removal treatment by other less energy-intensive methods, such as air drying. Based on these findings, we propose a concept of an oil sands tailings dewatering process for targeted removal of different forms of water by specific methods. It consists of a sequential chemical treatment of FFT with a flocculant and coagulant, followed by gravity sedimentation and vacuum filtration to remove FW and IPW, generating 50–55 wt% solids filter cakes. This is followed by a microwave treatment with or without subsequent air drying to partially remove the VPW and SBW, achieving ≥65 wt% solids in filter cakes that would be ready for preliminary reclamation activities.
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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".