Discrete Event Simulation for Machine-Learning Enabled Mine Production Control with Application to Gold Processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Interdisciplinary barriers separating data scientists and geometallurgists have complicated systematic attempts to incorporate machine-learning into mine production management; however, experiences in excavating a vein-hosted gold deposit within the Alhué region of Chile have led to methodological advances, which is the subject of the current paper. These deposits are subject to several challenges, from increasing orebody complexity and decreasing gold grades to the significant geological uncertainty that is intrinsic to these systems. These challenges then translate to mineral processing, which is already dealing with increased environmental and technological constraints. Geological uncertainty causes stockout risks that can be mitigated by the approach that is developed within this paper, which features alternate operational modes and related control strategies. A digital twin framework based on discrete event simulation (DES) and a customized machine-learning (ML) model is proposed to incorporate geological variation into decision-making processes, including the setting of trigger point that induces mode changes. Sample calculations that were based on a simulated processing plant that was subject to mineralogical feed changes demonstrated that the framework is a valuable tool to evaluate and mitigate the potential risks to gold mineral processing performance.
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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 | 0.000 | 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 it