Capacity Planning Optimization Integrating Cut-Off Grade and Block Sequencing Under Economies of Scale, Cost Structures, and Grade Distribution Considerations
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 process has three major problems: (1) selection/determination of mining and processing capacities/rates, (2) cut-off grade(s), and (3) block sequencing.Currently, these problems are solved sequentially because of the large size of the problem.The sequential approach might undervalue projects.Despite significant knowledge accumulation on cut-off grade and block sequencing, capacity selection has usually been overlooked.In current practice, the capacity based on the financial resources of the investor is mainly used.This capacity selection method ignores:(i) qualitative/quantitative heterogeneity within the mineral deposit, (ii) the interdependencies between the problems (insoluble conundrum or circular loop), (iii) the effect of the economies of scale, (iv) the relationship between capacity and innovation, and (v) the relationship between mining and mineral processing capacities.As a result, mining operations frequently encounter under-capacity/over-capacity issues, resulting in profit losses.This thesis focuses on optimizing capacity planning by exploring its relationships with cut-off grade and block sequencing.Economies of scale represent a significant phenomenon that complicates capacity planning, especially when multiple interdependent capacities are involved.Rapid technological innovations First and foremost, I would like to extend my deepest gratitude to my supervisor, Professor Mustafa Kumral, for his exceptional guidance, immense knowledge, and unwavering patience.Despite his demanding schedule, he consistently took the time to warmly welcome me and clarify the critical concepts and aspects of my research.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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