Capillary Suction for Dewatering Oil Sands Mature Fine Tailings: Experimental and Modeling Results
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The main challenge to the efficient processing of surface-mined oil sands is the generation of large volumes of slow-settling mature fine tailings (MFT). Tailings pose a significant environmental risk, including failure of storage facility, air-borne emissions, and surface and groundwater contamination. There is no practical mechanism to economically, technically, and ecologically dewater tailings to sufficiently dry MFT for reclamation. In this work, we leverage the natural mechanism of capillary action over filter paper to increase the area available for evaporation. The filter paper accelerated the evaporation of ∼35% of the MFT water, leading to an ∼20% increase in solidification and 33–55% reduction in reclamation time. The thermodynamics of water evaporation and mass transfer are modeled to estimate the rate of evaporation. Model fitting shows good accuracy in estimating dewatering for both nonporous and porous substrates using one and two fitted parameters, respectively.
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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".