Investigation of Interburden Dilution on Oil Sand Processability
Bibliographic record
Abstract
Abstract Surface mining of oil sands is generally limited to areas where the overburden thickness is less than ~75 m. Oil sands consist of a mixture of coarse sands, fine mineral solids, clays, formation water, and bitumen. Usually the orebody varies from 20 to 90 m in thickness, and the oil sand formation deposit is not homogeneous as it is intermingled with clays lenses (interburden). Bitumen content has been traditionally used as an indicator of ore processability. In fact, ores with a bitumen content of below 7% are considered uneconomical for processing. In this study, four types of ores were selected to evaluate the effect of adding two types of interburden with high clay contents on their ore processability. Ore processability was tested using the batch extraction unit. It was found that the interburden dilution does not always detrimentally affect the recovery. Depending on the ore grade, interburden dilution may boost the recovery for coarse grained ores, and depress the recovery for average and low grade ores. Current operational use of a maximum allowable fraction of interburden blended into oil sand feed may be overly simplistic. The results suggest that dilution to a maximum fines content of the blend is required rather than to a maximum fraction of interburden. Interburden effect on tailings settling behavior was also studied.
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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 | 0.001 | 0.001 |
| 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".