DYNAMIC MODELLING OF A SAG MILL-PEBBLE CRUSHER CIRCUIT BY DATA-DRIVEN METHODS
Bibliographic record
Abstract
In a semi-autogenous grinding (SAG) mill and pebble crusher circuit, the behaviour of the comminution process is non-linear and time-varying due to wear and variations in the feed material. To describe such a complex system, data-driven models were introduced, along with a case study of a SAG mill circuit in Copper Mountain, British Columbia, Canada. This paper presented a mill power draw model using several regression algorithms like Artificial Neural Networks (ANN), K-Nearest Neighbours (KNN), Random Forest (RF), and Gradient Boosting method (GBM). The results were then combined by weighted mean squared errors to perform a more accurate ensemble model. The second method considered the comminution process as a time series problem, so the original dataset was re-structured, and a forecasting SAG power model was proposed by using Long Short-Term Memory algorithm (LSTM). This LSTM forecasting model applied 20 minutes of historical data to predict 2-minute SAG power draw in advance. It was shown that both the presented methods gave promising results for SAG mill power prediction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".