Atmospheric leaching of enargite in iron sulfate solutions catalyzed by activated carbon
Bibliographic record
Abstract
Traditionally, smelting has been the primary method of treatment for copper sulfide concentrates. In modern smelters the environmental problem of sulfur dioxide emission has been addressed effectively, but pyrometallurgical treatment of concentrates containing elevated levels of arsenic is still difficult and costly. Therefore, arsenic is considered a penalty element for smelters. However, the depletion of “clean” (non-arsenical) copper deposits and the increasing demand for copper will make the treatment of copper-arsenic sulfides such as enargite and tennantite unavoidable. Thereby a viable processing method is required. Hydrometallurgical treatment of enargite using atmospheric leaching promises a comparatively simple method for managing arsenic by co-precipitating it with iron in the form of scorodite. The major challenge involved with this option is the slow rate of enargite leaching. A novel treatment for enargite-rich copper concentrates through atmospheric ferric leaching catalyzed by activated carbon is presented in this study. Enargite concentrates from three different sources in Chile and Peru and one enargite mineral sample from the United States were used in the leaching experiments. Batch leaching tests were conducted in sealed, jacketed, glass stirred-tank reactors. The results showed that enargite leaching was up to 6 times faster in the presence of activated carbon, making it possible to achieve virtually complete copper extraction within 24 hours. SEM studies revealed changes in the morphology of the passive layer on enargite particles which is formed as a product of leaching. The laboratory-scale tests indicated that desirable leaching kinetics could be maintained after recycling activated carbon particles multiple times to new leaching tests and also at a carbon:enargite concentrate mass ratio as low as 0.25. Activated carbon loss was reduced from 26 % to 5 % of the initial carbon mass by decreasing the impeller speed from 1200 rpm to 800 rpm, while the leaching performance remained similar. The effects of concentrate grind size, solution redox potential and initial total iron concentration on copper extraction have also been studied. The presented method promises a commercially attractive route to treat enargite concentrates.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".