Short-term planning of open pit mines with Semi-Mobile IPCC: a shovel allocation model
Bibliographic record
Abstract
In-pit crushing and conveying (IPCC) is considered a suitable alternative to truck haulage in open pit mines because it offers a lower operating cost than a truck-shovel system. It also reduces truck haulage distance and truck requirements. One of the IPCC variations is the semi-mobile system, which is relocated every two to five years. The short-term plan needs to be updated accordingly, based on the crusher’s optimal location and relocation time. To the best of our knowledge, short-term planning with IPCC is an area of research that has not been explored extensively yet and hardly any model can generate short-term schedules considering an IPCC in place. This research work proposes a mixed integer programming model to generate short-term production plans and near-optimal shovel allocation to mining faces, within a time horizon of 12 months. The objective of the model is to minimise the cost of material handling and maximise revenue, with respect to plant requirement, maximum allowable tonnage variation and IPCC location constraints, and the production and NPV targets set by the strategic plan. An iron ore mine case including a semi-mobile IPCC (SMIPCC) system with one relocation is used as the case study to verify the proposed model. The comparison of results between scenarios with and without IPCC justifies the use of IPCC in the iron ore mine from a short to medium-term perspective. The project can be considered a pioneering work in the arena of short-term mine planning with the IPCC.
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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".