Impact of the feed particle size distribution and its packing characteristics on compression comminution
Bibliographic record
Abstract
Due to its lower energy consumption than a conventional grinding circuit, there is an increased interest in the mining industry in the High-Pressure Grinding Rolls (HPGR) technology. Despite the benefits, the high quantity of material required to size the HPGR makes it difficult for new mines to consider this technology. The piston press test has been used at The University of British Columbia to predict the behavior of the HPGR. It is possible to predict energy requirements, size reduction, and throughput by utilizing a small amount of material to size the HPGR. The bulk material’s particle size distribution (PSD) plays an important role in how the particle bed will pack. This study investigates the differences in compressing three different sample PSDs obtained from the same copper ore crushed to −12.5 mm. The first PSD corresponds to the natural distribution resulting from the crusher. The second is created artificially to match the Fuller curve PSD, which theoretically should have the highest packing density. The third corresponds to a truncated feed produced by removing the fines (− 300 μ m ) from the natural feed. Tests were performed using a piston-and-die press test apparatus to compress the samples at different force levels. Entirely different behaviors are obtained while compressing the same material with different PSDs. Particularly, the Fuller curve PSD achieves a 31%–40% higher grinding rate than the natural feed, while the truncated feed achieves a 9%–30% higher grinding rate than the natural feed. Differences in the specific energy consumption, grinding efficiency, and final compacted densities highlight the critical influence of feed particle size distribution on the energy usage and generation of fine particles, underscoring the importance of optimizing these parameters for energy-efficient mineral processing. The overall findings indicate that an HPGR feed particle size distribution that matches the Fuller curve and, therefore, has a maximum packing density, results in the most energy-efficient comminution. • The comminution performance for different PSD is assessed with the piston press. • Different PSDs show distinct compression behaviors and comminution performance. • Fuller curve feed optimizes fine particle generation, improving material packing. • Truncated feed consumes more energy, highlighting the PSD impact on energy use. • The HPGR product tends to converge to a PSD that optimizes the material packing.
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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 | 0.000 |
| 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".