Methodology and Model to Predict HPGR Throughput Based on Piston Press Testing
Bibliographic record
Abstract
Sizing High-Pressure Grinding Rolls (HPGR) requires a large quantity of material, making it not attractive and costly to be considered for new mining projects regardless of their energy consumption reduction benefits. Ongoing efforts are being made at the University of British Columbia to predict the behaviour of the HPGR using a low quantity of material on a piston-and-die press apparatus. Although the energy requirements and size reduction predictive models are already developed, there is still a need to predict the HPGR throughput on a small-scale test. This paper presents a new model to predict the HPGR throughput based on the previously developed model to predict the operational gap by using less than 2 kg of sample. The throughput model was developed using machine learning techniques and calibrated using pilot-scale HPGR tests and piston press tests. The resulting model has an R2 of 0.91 with an average prediction error of ±4.2%. The developed methodology has the potential to fill the gap of the missing throughput model. Further pilot-scale HPGR testing is required to continue validating the model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".