Joint stochastic optimisation of short and long term mine production planning: method and application in a large operating gold mine
Bibliographic record
Abstract
A new multistage stochastic mine production scheduling approach is developed and tested in a large operating gold mine. The approach takes short scale orebody information in the form of grade control data into account. As simulated orebodies used in stochastic long term mine planning are based on sparse exploration data while grade control data are unavailable at the time of production scheduling, the short scale information is simulated stochastically. Stage 1 of the approach simulates high density future grade control data based on exploration data and grade control in previously mined out parts of a deposit. In stage 2, the technique of conditional simulation by successive residuals enables preexisting simulated orebody models to be updated using the simulated future grade control information. Stage 3 is based on a stochastic programming mine scheduling formulation that handles jointly multiple simulated orebody models from stage 2, and accommodates both maximising net present value (NPV) and minimising deviations from expected production targets. Stage 4 includes quantification of risk in the production schedules generated, comparisons and reporting. The application at a large operating gold mine demonstrates that the proposed approach is practical, and adds value to the operation. The approach is shown to deliver additional ore (3·6 Mt more) and metal (2·6 Mg more) which matches the mines reconciliations, unlike the conventional schedule, and results in a cumulative NPV which is on average 7·7 million AUD higher than that of a stochastic schedule without the simulated grade control data. The NPV is 230 million AUD higher compared to the NPV from the actual schedule of the mine. An additional key contribution of the proposed approach is the compliance of short- to long term production schedules and performance, leading to higher probability of meeting production targets and increased productivity. Given the capital intensity of mining projects, this contribution can be critically important to mining operations.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".