Liberation analysis of South African Middle group seam chromite ore processed with Vertical Shaft Impactor Crusher
Bibliographic record
Abstract
In this study, the impact of vertical shaft impactor (VSI) crusher on fine and ultrafine production during crushing of South African Middle group (MG) chromite seam was investigated.The chromite ore was subjected to size reduction with VSI crusher at varied rotor tip speed (30, 35 &40 Hz) and particle size distribution analysis was done on all the three products.The VSI crusher product particle size distribution indicated that as the VSI rotor speed was increased, the amount of ultrafine generated also increased.The product at rotor speed of 35Hz revealed a lower percentage of ultrafines at 6.16% in the -212+150µm and 0.85% in the -150+53µm particle size range.Ball mill product from the Plant was analysed and the results revealed that the ball mill is more efficient for chromite ore liberation as 84% of the -425+300µm was produced compared to the VSI which only produced 51.27% -425+300µm.However, the amount of ultrafines produced was high at 9.2% of -150+106µm size fraction, with most ultrafine particle size of 4.42% in the -53+26µm size fraction.The mineral liberation analysis conducted on the -425+300 and -300+212µm VSI crusher products indicated that the degree of chromite liberation was higher at 95% on the -300+212µm size fraction compared to 78% liberation on the -425+300 µm size fraction.From the liberation analysis conducted in this investigation, it can be concluded that VSI crusher has a greater potential in chromite size reduction as it has shown to produce a liberated product with reduced percentage of ultrafines.However more test needs to be done on the VSI residence time to improve the percentage mass of the required liberated product particle size.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".