Adaptive simultaneous stochastic optimization of mining complexes: Where is the value coming from?
Bibliographic record
Abstract
This paper aims to identify the sources of value created in the strategic plan of a mining complex when the adaptive simultaneous stochastic optimization of mining complex (ASSOMC) approach is used. This approach considers operational and investment alternatives dynamically within the simultaneous stochastic optimization of mining complex (SSOMC) framework, providing an adaptive strategic plan that manages technical risk and maximizes value. A case study on a world-class mining complex illustrates the effects of this optimization model, comparing the adaptive alternatives of the ASSOMC with the fixed SSOMC case. Results show that considering the SSOMC without alternatives was a starting point, including either investment or operational alternatives in a fixed manner, provides an increase in NPV of 4.4 % and 2.8 %, respectively; whereas considering both jointly increases the NPV by 10.3 %. On the other hand, when adaptive changes are considered over investments, such as additional crushers or conveyor belts, the NPV increases further, by about 20 %. The focus is placed on identifying the location and components where this extra value is created within the mining complex, understanding the effect that the alternatives have, and capitalizing from them. This study finds that, due to the non-linear synergies that exist between the different components of a mining complex, the adaptive aspect of the approach allows the production plan optimization to be proactive and to tailor its configuration according to possible changes and future developments. • Adaptive simultaneous stochastic optimization of mining complexes with operational and investment alternatives. • Strategic planning of a mining complex and identification value creation sources. • Direct risk management of material uncertainty and variability when optimizing mining complexes dynamically. • Proactive production planning optimization tailoring configuration based on possible changes and future developments. • Case study on a world-class mining complex showing identification of alternatives and capitalizing from them.
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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".