Novel optimization models for surface and underground mine planning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mine planning and optimization affect efficiency, profitability and productivity of operations significantly.Low commodity prices, high resource degredation maintenance costs and high fixed infrastructure costs necessitate the use of optimal decision making tools for mining companies to make profit.All mines have different characteristics and planning phases.In this research, different optimization problems that suit various mining techniques and planning stages are studied.In essential, there are two types of mining: surface mining and underground mining.Surface mining operations are generally long-term because overburden must be removed to access the profitable orebody.This requires strategic long-term planning at the feasibility stage.The first publication in the scope of this research focuses on long-term surface mine planning with environmental considerations.The provided solution optimizes the problem using mixed integer linear programming (MILP).When operation starts and bench sectors are mined on a daily basis, the need for short term planning arises.The second publication addresses the dig-limit optimization problem, which is an important part of short-term planning.With the proposed MILP optimization method, the ore-waste boundaries are delineated with the equipment size constraints.Although underground mining also starts with exploration and resource estimation/simulation stages, the problems that need to be addressed are very different from surface mining techniques and it has its own unique challenges.Special focus is given to the subi I would like to express my gratitude to Prof. Mustafa Kumral for being an exceptional supervisor with his invaluable guidance and insights.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| 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 it