An Integrated Optimization Model for Strategic Open-Pit Mine Planning and Tailings Management
Bibliographic record
Abstract
A strategic mine planning model determines the best order of extraction and destination of material over the mine-life, in a way that maximizes the net present value of the produced minerals. In case of oil sands open-pit mining, further processing of the extracted oil sands generates massive volumes of slurry containing water, sands, clay and fine material known as tailings. Since the tailings volume significantly influences the mine production and site reclamation, it is reasonable to consider tailings management within the frameworks of long-term mine planning. One of the current practices in Alberta oil sands industry is to process the tailings slurry and make composite tailings (CT), through adding coagulant aids to the mature fine tailings (MFT), to accelerate its dewatering and make it ready for reclamation. To save space and also to avoid higher reclamation costs, the processed tailings is deposited in in-pit tailings containments constructed by internal dykes using mine waste material. In this research, an integrated mine planning framework is proposed, implemented and verified using mixed-integer linear programming technique, to optimize the production schedule with respect to mine waste management in terms of dyke construction and in-pit tailings deposition. A tailings model is developed and integrated to the mine planning model that calculates the volume of tailings slurry and composite tailings based on the processed material. Two small case studies and one large-scale case are carried out to verify the performance of the proposed optimization model. Two variable reduction techniques are implemented to increase the efficiency of the run time. The model solves the large-scale problem to optimality over 30 periods within 0.5 to 1.5 hours of CPU time, depending on the model resolution. In the generated schedule, the produced tailings is being deposited in the excavated mining-pit as the mining operations proceed and the in-pit dykes are constructed using mine waste material.
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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 | 0.001 |
| 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".