Optimization in Mineral Processing: A Novel Matheuristic for a Variant of the Knapsack Problem
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Bibliographic record
Abstract
This study introduces a novel heuristic approach to optimize mineral processing in metallurgical plants, framed as a variant of the fractional knapsack problem. The optimization framework integrates plant operational modes, blending requirements, and processing constraints to maximize the recoverable value of mineral blocks while adhering to plant capacity and feed limitations. Building on a previously established mixed-integer linear programming formulation, this study develops a heuristic algorithm employing a greedy strategy. This alternative approach significantly reduces computational time while achieving near-optimal solutions, making it suitable for practical implementation. Validation through a case study demonstrates the algorithm’s effectiveness in managing complex constraints and delivering actionable insights for real-world operations. These findings highlight the potential of this methodology to streamline the mineral processing stage of broader mine planning frameworks, complementing the initial optimization of block extraction with faster and more reliable processing calculations.
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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) | 0.000 | 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 it