Semi-mobile in-pit crushing and conveying vs. truck-shovel systems: Long-term scheduling with road and conveyor networks integration
Bibliographic record
Abstract
In-pit crushing and conveying systems (IPCC) integrate crushing and conveying directly into the transportation of the extracted material from the pit, minimizing the need for extensive truck fleets and haulage infrastructure that is typical in Truck Shovel systems (TS). This approach reduces truck-related costs and environmental impacts while enhancing operational efficiency. The current study optimizes long-term scheduling in open-pit mining operations by comparing IPCC and traditional TS systems. Our methodology employs two mathematical models optimization model to determine optimal crusher locations or crusher panels and establish a practical long-term extraction sequence. Through a comprehensive case study involving pushbacks and analyzing different road and conveyor network configurations, we examine the capital and operational costs across four scenarios for the in-pit crusher: without any in-pit crusher, with an ore in-pit crusher, with a waste in-pit crusher, and with both ore and waste in-pit crushers. Results include comparisons of Net Present Value (NPV), tonne-kilometers traveled, total kilometers traveled, and the number of trucks required. Significant improvements in NPV are observed in scenarios with both ore and waste crushers, reflecting reduced hauling distances and operational costs. The waste crusher scenario also demonstrates substantial savings, while the ore crusher scenario shows moderate improvements compared to the base case without any in-pit crusher.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".