Investigating the benefits of using <scp>HPGR</scp> prior to cyanidation of low grade gold ore
Bibliographic record
Abstract
Abstract It is known that the cracked structure of the particles has positive effects in leaching processes. In this respect, high pressure grinding rolls (HPGR) provides some advantages such as increasing leaching efficiency and reducing leaching time by creating more cracked particles compared to other crushers. Although there are studies on the advantages of HPGR on different ores, these have not been fully demonstrated in low grade gold ores, and leaching kinetics have not been examined in detail in the studies conducted in this context. In this study, first of all, crack formation in a low grade gold ore by crushing with HPGR and jaw crusher was investigated. Then, bottle roll tests were performed in different size fractions and different cyanide concentrations along with column leaching of the pelletized ore in a single test condition. As a result of the studies, it was determined that 2–3 times more cracked particles were formed in HPGR depending on the particle size. It was shown that the modified Kelsall model explains the kinetics better than other kinetic models tested. In summary, it was revealed that maximum gold extractions are almost same for HPGR and jaw crushers. However, the fast dissolution kinetic constant was found to be around 25% higher for HPGR than for the jaw crusher in bottle roll tests while this ratio was approximately 30% higher in column leaching tests, suggesting a distinct influence of the crushing method on the dissolution kinetics.
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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 | 0.001 |
| 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) | 0.001 | 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".