Mine schedule optimisation with ventilation constraints: a case study
Bibliographic record
Abstract
This paper describes a process for optimising mine schedules with ventilation constraints using the Schedule Optimization Tool (SOT), the Ventilation Constraint Module (VCM) and a ventilation network solver (Ventsim). The VCM was developed as a component of MIRARCO’s SOT+ research project. Proper ventilation is critical for underground mines to operate safely, yet it is often not adequately considered early in the mine planning cycle. The need to account for ventilation early in the long-term planning cycle motivated the development of the VCM. The VCM generates constraints on the schedule of mining activities for each zone and for each stage of the mine life, based on available airflows. These constraints are intended to ensure that a mine schedule will be feasible from a ventilation perspective. Adhering to these ventilationbased constraints, as well as other constraints related to the project, SOT maximises the net present value (NPV) of the long-term schedule. The VCM will also assist the user in identifying opportunities to redistribute the airflow in the mine in ways that support higher-NPV schedules. A case study for a hypothetical underground mine will be presented, showing that the VCM supports the generation of optimised life-of-mine schedules that adhere to realistic ventilation constraints.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".