Electrodialytic extraction of copper, lead and zinc from sulfide mine tailings: Optimization of current density and operation time
Bibliographic record
Abstract
• Cu, Pb and Zn were electrodialytic extracted from two sulfide mine tailings. • Up to 97 % Pb, 88 % Zn and 70 % Cu were extracted but differed among tailings. • Mine tailing material and metal characteristics controlled the extraction. • These affected the operation time and current density of the electrodialysis. Re-mining unextracted metals from mine tailings can contribute to sustaining society’s high demand for metals. Electrodialysis (ED) is a promising, yet underdeveloped, extraction technology for mobilizing and transporting particle-bound elements in a suspension slurry. This study investigated ED for extraction of copper (Cu), lead (Pb) and zinc (Zn) from Cu mine tailings (concentration of Cu: 2039 mg/kg, feldspar-dominant) and Pb-Zn mine tailings (Pb: 4469 mg/kg, Zn: 19417 mg/kg, dolomite-dominant). A multivariate experimental design was used to examine the effect of varying current density (1–5 mA/cm 2 ) and operation time (24–336 h) of 40 bench-scale experiments. The maximum extractions obtained were 70 % Cu from the Cu mine tailings and 97 % Pb and 88 % Zn from the Pb-Zn mine tailings. Depending on the tailing, the extraction was influenced differently by current density and operation time. In particular, carbonate minerals such as dolomite and calcite delayed acidification and thus the extraction due to their buffering capacity. However, as the carbonaceous tailings contained a larger proportion of soluble metals, likely to be bound in the carbonates themselves, overall higher metal extraction was obtained from these. Thresholds above, for which the increase of current densities did not increase extraction, were observed at 2 and 4 mA/cm 2 for the Cu and Pb-Zn tailings, respectively. The solubility of the remaining metals in the treated tailings increased significantly due to acidification by the EDR process and utilization of the treated tailings in construction materials therefore needs further investigation.
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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) | 0.000 | 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".