Stochastic optimisation model for open pit mine planning: application and risk analysis at copper deposit
Why this work is in the frame
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Bibliographic record
Abstract
Life of mine (LOM) production scheduling is a critically important part of open pit mining ventures and deals with the efficient management of cash flows in the order of hundreds of millions of dollars. A LOM production schedule determines the quantity and quality of ore and waste materials to be mined over time, so as to maximise the net present value (NPV) of the mine. Life of mine production scheduling is an intricate and complex problem to address and it is adversely affected by geological risk, which can, however, be accounted for and managed while constructing production schedules. In the present study, the LOM scheduling process of a disseminated copper deposit demonstrates the intricacies of a new scheduling approach based on the technique of simulated annealing and stochastically simulated representations of the copper orebody. The study documents the benefits of incorporating geological uncertainty in the mine scheduling process through the proposed approach. The stochastic approach is found to generate a LOM schedule with a NPV 26% higher than that of the conventional schedule. Risk analysis results show that the stochastic schedule has low chances to significantly deviate from targets; the probability that the conventional schedule will deviate from production targets is high. In addition, comparisons show that the conventional scheduling approach overestimates ore tonnages and underestimates the NPV of the mine design. The findings of this study suggest that LOM schedules that incorporate geological uncertainty lead to more informed investment decisions and improved mining practices.
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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 | 0.001 | 0.001 |
| 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 it