Numerical simulation of fine oil sand tailings drying in test cells
Bibliographic record
Abstract
As a promising technology in disposal of mature fine tailings (MFT), atmospheric fines drying (AFD) is currently being implemented on a commercial scale at Shell Canada’s Muskeg River Mine near Fort McMurray, Alberta. AFD involves the use of a polymer flocculent to bind fine particles in MFT followed by thin lift sub-aerial drying. Upon deposition, the tailings-polymer mixtures are subjected to atmospheric conditions. The disposed layers undergo a cyclic drying and rewetting process due to precipitation and deposition of additional lifts on top of the dried layer. The current research aims to numerically simulate this process, including both periods of drying and wetting. An existing numerical drying model was extended with a realistic rewetting behavior, based on the shrinkage and water retention curves for drying as well as for rewetting. This improved model, that was validated with test results from laboratory columns, was used to simulate a large scale drying field test that is currently being performed by Shell Canada. The results of the simulation are published here without prior knowledge of the results by the authors, forming a Class A prediction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".