Investigating the use of the steel wheel abrasion test for ore characterization
Bibliographic record
Abstract
In mineral beneficiation, grinding is used to reduce the size of the ore particles, generate larger surface area, and liberate valuable components.Grinding represents the most energy consuming stage of the process.Continuous research has aimed to increase the rates and efficiencies of milling by predicting the behaviour of large industrial mills, for example, through an examination of energy-size relationships and by using population balance models.The input for these models comes from data extracted from small laboratory-scale mills or impact tests.However, these breakage-testing techniques do not properly describe the behaviour of industrial mills, since these techniques cannot achieve the necessary modes of breakage or the correct input forces.Since the steel wheel abrasion test (SWAT) produces particle size reduction through abrasion breakage under a wide range of controlled input forces, this thesis investigates the use of the SWAT for ore characterization.This test is a variation of the dry and wet sand/rubber wheel abrasion test, a standard for measuring mass loss in metallic materials subjected to scratching abrasion.This thesis studies three different materials tested in the SWAT under the t 10 ore characterization scheme.The results show that the t 10 model can be properly fitted to the data collected from the tests.Two of the ores also were analyzed by using the drop weigh test (DWT), and the resulting parameters were compared to those from the SWAT, which showed that a relationship between the resulting parameters could be established.However, the results suggested that it seems more promising to use the SWAT to determine the abrasion breakage component and to calculate the breakage function of the small particles (below the inflection point), a function that otherwise would be unattainable in the DWT.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".