Copper Anode Slime Processing with a Focus on Gold Recovery: A Review of Traditional and Recent Technologies
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Bibliographic record
Abstract
Copper anode slime (CAS) contains high concentrations of precious metals, particularly gold, which can reach up to 11 wt%. During copper anode electrorefining, 5–10 kg of CAS is generated per ton of copper cathode. Processing CAS is crucial for economic reasons, as gold significantly contributes to revenue for both miners and custom copper smelters. This paper provides a comprehensive review of industrial processes and technologies for CAS treatment, with a focus on gold recovery, covering studies from the early 1930s to the present. It documents traditional and recent trends and analyzes the advantages and disadvantages of existing methods. Key factors affecting revenue, such as gold production lead time, in-process inventory, and first-pass recovery rate, are discussed to mitigate losses in fluctuating gold markets. CAS processing routes are categorized into two main groups: traditional hybrid routes (involving hydrometallurgy, pyrometallurgy, and electrorefining/electrowinning) and rather recent purely hydrometallurgical routes. Traditional methods can take up to 45 days, with gold recovery occurring late in the process and losses arising in the anode, cathode, electrolyte, or slag. In contrast, purely hydrometallurgical routes have total processing times of 7–8 days, achieve early gold recovery, and can attain first-pass recovery rates as high as 99%. Additionally, the hydrometallurgical routes are more environmentally friendly, with lower pollution levels and reduced energy consumption compared to hybrid routes. These findings indicate that purely hydrometallurgical routes outperform traditional hybrid methods. This paper aims to serve as a guideline for industrial CAS processing, assisting custom copper smelters in navigating challenging market conditions marked by low treatment and refining charges, with an emphasis on enhancing gold recovery to promote sustainability.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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