Application of Multivariable Data Analysis in Mineral Processing
Bibliographic record
Abstract
Data analysis and application of machine learning (ML) have demonstrated successful performance in various data rich industrial applications. Mineral processing and metallurgical operations are considered suitable for implementation of novel ML-based algorithms. The key operating performance and product outputs are usually obtained from the lab measurements and analyses that can be expensive, complex, and time consuming. Therefore, the development and application of a soft sensor and/or a state observer is a useful option to be considered due to their ability to provide the distribution of desired outputs in a continuous manner. In addition, the motivation to apply a soft sensor (a data-based model) is to provide guidance and/or information feedback to the operator in charge of making operational decisions. The soft sensor was developed at Sherritt's Metal Plant in Fort Saskatchewan as a nonlinear neural network model and it was based on two years of plant historical data. The model was also validated based on historical data, live testing, and additional sampling of process streams during simultaneous sampling campaign.
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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 | 0.000 | 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".