An application of mathematical programming and sequential Gaussian simulation for block cave production scheduling
Bibliographic record
Abstract
The current trend of deeper and lower-grade deposits makes open pit mining less profitable. Mass mining alternatives have to be developed if mining at a similar rate is to be continued. Block cave mining is becoming an increasingly popular mass mining method, especially for large copper deposits currently being mined with open pit methods. After finding the initial evaluation of a range of levels for starting the extraction of block cave mining, production scheduling plays a key role in the entire project’s profitability. Traditional long-term mine planning is based on deterministic orebody models, which can ignore the uncertainty in the geological resources. The purpose of this paper is to present a methodology to find the optimal extraction horizon and sequence of extraction for that horizon under grade uncertainty. The model does not explicitly take into account other potential project value drivers such as waste ingress into the draw column or the impact of primary or secondary fragmentation on either production or recovery. Maximum net present value (NPV) is determined using a mixed-integer linear programming (MILP) model after choosing the optimum horizon of extraction given some constraints such as mining capacity, production grade, extraction rate and precedence. Application of the method for block cave production scheduling using a case study over 15 periods is presented.
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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".