Meta-Model Development for Mine-to-Mill Optimisation Using AI and Simulation
Bibliographic record
Abstract
The current demand for mineral resources is higher than it has ever been, and it is expected that, over time, the quality of future resources will decline, and they will become more difficult to extract.Routinely collected on-site data from various mining stages are often neglected in mining operations and are not being used to improve the value of the mining chain.To address this issue, mining companies need to increase the efficiency of their mining processes to achieve sustainable production by using innovative solutions.The primary purpose of the study presented here is to develop an integrated knowledge-based system using advanced AI techniques to simulate, monitor, assess, and optimise mining processes from blasting to downstream products.In this study, publicly available data from the Barrick Cortez Mine in Nevada, USA, was used to model the entire mining process from blasting to SAG mill by using Orica's Integrated Extraction Simulator (IES) platform.The comparison of real data from the mining site with simulated data on the IES platform demonstrates that the modelled operations closely match the real data.Thirteen parameters related to blasting, screens, crusher, and SAG mill were considered.Given the computational infeasibility of testing all combinations, three million scenarios were simulated to identify key performance drivers.Machine learning models-including linear regression, decision trees, random forest, and XGBoost-were evaluated to determine the most effective for predicting outcomes.The next step involved using input scenarios and outcomes to investigate key features and interpret results using feature importance and SHapley Additive exPlanations (SHAP) techniques, respectively, as powerful tools for determining the influence of individual features of the models.The findings highlight the potential of AI-driven meta-models to enhance decision-making, reduce operational costs, and improve resource usage in mining operations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".