Reliability-based performance analysis of mining drilling operations through Markov chain Monte Carlo and mean reverting process simulations
Bibliographic record
Abstract
In recent years, commodity prices have swiftly decreased, narrowing the profit margin for many mining operations and forcing them to find effective cost management strategies to respond to low prices. Given that equipment is one of the most significant assets of a mining company, efficient equipment utilization has strong potential to reduce costs. This paper focuses on the relationship between the number of available drilling machines based on reliability analysis and the number of holes to be created on a bench of an open pit mining operation. Since equipment availability is random in nature, a range of holes to be drilled corresponding to a specified probability level was determined. To assess the performance of the proposed approach, a case study was carried out using two stochastic modeling techniques. Evolutions of reliabilities of 10 rotary drilling machines over a specific time were simulated by Markov chain Monte Carlo and mean reverting processes, using historical data. Multiple simulations were then used for risk quantification. Results show that the proposed approach can be used as a tool to assist production scheduling and assess the associated risk.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".