Electrokinetic and chemical treatment of oil sands mature fine tailings: dewatering and strengthening
Bibliographic record
Abstract
Oil sands tailings have become a major challenge in Canada due to various geotechnical and environmental concerns. Mature fine tailings (MFT) typically contain 70% water and 30% silt- and clay-sized solids and have low hydraulic conductivities, which lead to difficulties in consolidation by nature. Electrokinetics (EK) has been proved to be effective at dewatering and strengthening oil sands tailings, but is only limited to the anode area. Further treatment is needed to improve the tailings around the cathode. Chemical stabilisation is a mature technique for ground improvement. However, there is limited information for the combined effects of EK and chemical treatment on high water content geomaterials such as MFT, slurry, sludge and so forth. This research study was carried out to evaluate the effects of combined EK and chemical treatment of MFT. Quicklime and Portland cement were selected as the chemical additives. The changes in water content, undrained shear strength, plasticity, pore-water pH and electrical conductivity and particle zeta potential after treatment were studied. It was found that the chemical treatment reduced the material property difference between the anode and cathode, whereas it reduced EK-induced water flow. Combined EK and chemical treatment of MFT may be beneficial at a low chemical dosage.
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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".