Simultaneous stochastic optimisation of mining complexes with equipment uncertainty: Application at an open-pit copper mining complex
Bibliographic record
Abstract
A mining complex or mineral value chain is an integrated system composed of mines, stockpiles, waste disposal and tailings facilities, processing destinations and transportation, that leads to generating sellable products delivered to customers and/or the spot market. To deal with such a system, conventional approaches optimise the related components independently and sequentially, while ignoring the related uncertainties. This article extends the simultaneous stochastic optimisation of mining complexes, so as to incorporate equipment uncertainties in addition to supply uncertainty. The inclusion of multiple components and different sources of uncertainty empowers the optimisation to capitalise on the synergies between the different components of a mining complex, while also managing the related technical risk and maximising the net present value. An application at a copper mining complex demonstrates the applied aspects of the proposed approach that jointly considers supply and equipment uncertainty to generate life-of-asset production schedules with a 2% higher net present value, when compared to the results considering only supply uncertainty.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".