Application of simultaneous stochastic optimization with geometallurgical decisions at a copper–gold mining complex
Bibliographic record
Abstract
Simultaneous stochastic optimization of mining complexes aims to optimize its different components in a single optimization model under grade, geometallurgical and material type uncertainty. The single optimization model capitalizes on synergies between the different components and the quantified variability and uncertainty of the materials mined, to better meet production targets while maximizing the net present value (NPV) of a mining complex. Integrating uncertainty and decisions about geometallurgical aspects of materials in the optimization model assists in achieving higher and more stable throughput with comminution circuits. This paper introduces an approach to integrate uncertainty and decisions about two non-additive geometallurgical properties, semi-autogenous power index and bond work index in the simultaneous stochastic optimization model. An application of the proposed approach at a large copper–gold mining complex indicates higher chances of meeting the different production targets, substantial increase in metal production and a 19.3% increase in NPV compared to the conventional plan.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".