Cost optimization of drilling operations in open-pit mines through parameter tuning
Bibliographic record
Abstract
Problem statement: Increasing efficiency and performance of bench drilling in open-pit mines has the potential to generate considerable savings. This increase can be realized by monitoring the drilling operation, analyzing monitoring data with statistical tools and optimizing operational variables. Finding the best configuration of controllable drilling parameters (e.g. rotation speed, pulldown force and bailing air pressure) and determining replacement time of drill bits would assist to increase penetration rate and optimize drilling operation cost.Approach: In this paper, the effects of controllable variables on drilling performance are quantified by the face-centered central composite design, and the replacement time of drill bits is optimized using the genetic algorithm.Results: Results show that the proposed approach could be used to determine the optimal drilling parameters and minimize the energy cost in open-pit mines.Abbreviations: FCCCD: face-centered central composite design; RPM: revolutions per minute; PDF: push down force; BAP: bailing air pressure; ANOVA: analysis of variance
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".