Evaluating the semi-mobile in-pit crusher option through a two-step mathematical model
Bibliographic record
Abstract
Utilizing the in-pit crushing and conveying (IPCC) system presents a potential solution for reducing the substantial operational costs associated with truck and shovel operations in open-pit mining. Identifying optimal placements for in-pit crushers throughout the mine life establishes a new series of constraints for mine planning. In this paper, a two-step mathematical model is proposed to minimize the haulage costs. In the first stage, the best-nominated locations of the crusher or the optimum crusher panels are determined. In the second stage, a mine schedule honoring the spatial precedence for the optimum crusher panels is proposed. The objective of this work is to evaluate the IPCC option through a mathematical optimization model, utilizing the road network and proposing a practical rather semi-optimal crusher location. As open pit mining becomes deeper, the need for IPCC implementation becomes more critical that can be truly explored through the road network proposed in this study. The model is verified by a real iron ore mine case study for three scenarios without IPCC, ore IPCC, and ore and waste IPCC. The findings indicate that at the culmination of the ninth year of extraction, a substantial 55% disparity in ore tonne-kilometers transported is observed between scenarios involving the presence of at least one IPCC and those without any IPCC. Furthermore, a notable difference of 110 km of travel emerges in the overall distance when comparing scenarios incorporating both ore and waste IPCCs versus those lacking IPCC. • Two-step mathematical model for open pit mining with IPCC under three scenarios. • Creating practical units for placing the in-pit crusher named “Crusher Panel”. • Suggesting near-optimal extraction sequences and estimating relocation times. • Integrating the actual road network of the mine into the decision-making process. • Accounting for the geometric characteristics of the crusher, through crusher panels.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".