Bibliographic record
Abstract
One of the challenges faced by the oil sand industry is the dewatering of mature fine oil sands tailings (MFT) due to the low permeability. Vacuum pressure or electrokinetic (EK) dewatering is effective to meet this objective. This research was focused on the experimental study and analytical modelling to explore the influence of combined vacuum and EK treatment on MFT. In this study, systematical experiments on MFT recovered from the northern Alberta were carried out. At first, the characterization and EK cell tests were conducted to measure the basic properties and the coefficient of electroosmotic permeability (ke) of MFT. Secondly, dewatering experiments were carried out with three conditions, i.e. EK dewatering, vacuum pressure dewatering and surcharge preloading dewatering. In this phase of the research, experiments on dewatering by EK combined vacuum pressure treatment and EK combined surcharge preloading treatment were performed in an EK-vacuum cell designed and fabricated for this research. The results of dewatering treatment were assessed with the final water content, undrained shear strength and power consumption etc. Moreover，the theoretical analysis was carried out to examine the feasibility of one dimensional consolidation of MFT based on small strain theory under specific testing conditions. An analytical solution was derived, with variables including the surcharge pressure, vacuum pressure and voltage. The result of the experiments showed the positive effect of dewatering of MFT by EK combined vacuum pressure treatment. After the treatment, the undrained shear strength of MFT ` ii reached 5kPa along with significant reduction in water content, which can be as low as 27.7%. The theoretical model based on the small strain theory simulates the consolidate rate well; yet it is not able to accurately predict the final settlements. A further study incorporating the large strain consolidation theory is needed.
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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".