A Multi-Method Machine Learning Framework for Simulating Power Dynamics in an Iron Ore Cone Crusher Plant
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Bibliographic record
Abstract
Comminution, particularly post-crushing, is a critical phase in mineral processing that reduces run-of-mine ore to an acceptable particle size before undergoing a downstream beneficiation process. Despite its importance, this stage has been under-investigated probably due to challenges of sampling and data collection and inadequate technical and financial support. This research presents a multi-method machine learning framework designed to simulate the power dynamics within an industrial-scale iron ore cone crusher circuit. The framework integrates advanced artificial intelligence techniques to evaluate energy consumption and estimate cone crushers output K80 accurately. The developed framework spans from different algorithms by data preprocessing techniques such as normalization and Principal Component Analysis (PCA) to standardize and reduce the dataset’s dimensionality, coupled with multiple machine learning algorithms, including Gradient Boosting Machines (CatBoost, LightGBM, XGBoost), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) networks. The study utilized real-time data collected over six months by considering the key operational parameters such as total tonnage, hopper filling levels, power draw, cone position, and crusher discharge settings. The modeling approach was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Asymmetric Loss (AL), and the coefficient of determination (R2) as performance metrics. Results indicated that the LSTM model outperformed other methods by achieving the lowest MAE and RMSE and the highest R2 score of 0.92, thereby demonstrating superior precision in estimation. However, XGBoost showed optimal performance in managing asymmetric loss, highlighting its efficiency in error management across different scenarios. Model residual analysis, performance metrics box plots, and scatter plots comparing measured and estimated data across the models are also provided for further analysis. This study demonstrates the transformative potential of machine learning models in refining industrial process simulations, ultimately driving enhanced productivity and sustainability in the mining industry.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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