Optimisation of gold recovery from small scale custom mills
Bibliographic record
Abstract
Custom mill tailings generated during the treatment of gold ores in Custom milling plants contains a considerable significant amount of gold (Au) and Silver (Ag). The potential of recovering gold and silver from the tailings by cyanidation leaching process was investigated. The custom mill tailings were characterised using Malvern particle analyser, XRF, XRD and SEM-EDs. In this study, the MiniTab software experimental design method was used to determine the optimum leaching conditions. The PSD results revealed that the custom mill tailings are coarse with 80% passing 2000m. The tailings are a quartzite material containing minor amount of sulphides such as pyrite, chalcopyrite and pentlandite. The amount of these sulphides is very low ruling the probability of the tailings being refractory. Recoveries of 88 and 95% for gold and silver respectively were achieved leaching at 800g/t NaCN 8hrs. However, gold recovery above 85% was achieved by leaching in 600g/t NaCN for 8 hrs. Custom mill tailings have proved to be a source of precious metals and Min Tab software is a useful tool in optimisation of the leaching conditions.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".