Machine-Learning Analysis of the Canadian Royalties Grinding Circuit
Bibliographic record
Abstract
This work aimed to understand the relationships between grinding variables and the P80 (80% passing size) of a grinding circuit (feed to flotation). Canadian Royalties want to obtain and reduce variations in the P80, which is currently 65 micrometres. Thus, principal component analysis (PCA), part of machine learning, was utilized to better understand the factors that significantly influence the P80. PCA is meant to be used as a guideline for plant metallurgists to determine how the grinding circuit factors influence P80; thus, the variables can be manipulated to lower P80 fluctuations. PCA revealed that the head grade of the ore (pentlandite (Pn), chalcopyrite (Cp), pyrrhotite (Po) and non-sulphide gangue (NSG)) and the primary ball mill power were weakly correlated with P80. However, the ore level in the silo, flowrate to cyclones, cyclone pressure, percent solids and the feed tonnage rate to the primary ball mill were strongly correlated with P80. This information can be used to develop a strategy to control the P80 of the grinding circuit and provide a more consistent grind size to the flotation circuit, which can positively impact metallurgical performance.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".