Optimisation of gold recovery from small scale custom mills
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it