A Multiple Objective Hybrid Algorithm for Daily Ore Blend in Oil Sands Mines
Bibliographic record
Abstract
Oil sands mining contributes to the Canadian daily oil production by producing 1.617 million barrels per day. Processing oil sands is a complex operation with a critical sensitivity to the properties of the blended ore at the crusher that must follow the slurry pipeline and separation tank requirements. The blend optimisation in oil sands mines is a tedious work performed mostly manually by the mining engineers at the mine sites and requires fine-tuning as shovels move from one block to another in the same mining face. Miscalculations leading to deviation from the target properties cause inevitable economically and operationally expensive issues to the value chain including but not limited to sanding the pipeline, separation tank hick-ups, etc. Herein, we present a hybrid multi-objective algorithm addressing abovementioned issues in daily blending process and providing the operation crew with a clear practical production target at each mining face. The algorithm takes the processing targets as inputs and minimises deviations from each desired target by considering material properties at mining faces, the capacity of trucks, and production rates of active shovels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".