Copper leaching in chloride media with a view to using seawater for heap leaching of secondary sulfides
Bibliographic record
Abstract
Copper mines increasingly face the challenge of processing large amounts of low-grade sulfides of elevated concentrations of impurities. In most cases, the challenge is compounded by water scarcity. A potential strategy to address such challenge is to use seawater with elevated concentrations of chloride for heap leaching of secondary sulfides. To ensure success of such heap leach processes, we comprehensively investigated aqueous chloride solution properties at high ionic strength, kinetics of copper extraction from chalcocite in chloride media, and mechanisms by which various factors influence leaching rate, in both acidified ferric and cupric chloride media. The aqueous chloride solution properties were determined by thermodynamic calculations supported by laboratory ORP (oxidation-reduction potential) measurements. The leaching kinetics was quantified by conducting a series of reactor and column leaching tests under fully-controlled conditions. The mechanisms were uncovered using various surface characterization techniques, including SEM-EDX and XPS. The thermodynamic calculation determined the speciation of iron and copper at increasing chloride concentration up to 3 M, based on which the actual cathodic and anodic reactions responsible for copper extraction were proposed. The kinetics study showed that the leaching reaction slowed down after 70 – 80% of copper was extracted in both ferric and cupric chloride media at ambient temperature. Kinetic models were first developed to satisfactorily describe copper extraction as a function of ORP, chloride concentration, and temperature in reactors, and then scaled up to describe copper leaching performance in columns. The surface characterization results showed that sulfur sequentially transformed from monosulfide to disulfide, and then to polysulfide and elemental sulfur. The slow decomposition of polysulfide was responsible for the slow leaching at high ORPs, whereas a combination of polysulfide decomposition and diffusion barrier by elemental sulfur layer was the reason for the slow dissolution at low ORPs. The effect of chloride concentration on the reaction rate may only manifest itself at low ORPs where the level of the elemental sulfur crystallinity was lower. This body of knowledge would ultimately pinpoint possible options to optimize the leaching performance.
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.
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) | 0.000 | 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 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".