Impact of operational parameters on arsenic impurities in Sb2O3(s) and Bi2O3(s) recovery from copper electrorefining: A viable alternative to refiners?
Bibliographic record
Abstract
The copper pyrometallurgical industry faces growing challenges from elevated impurities such as arsenic (As), antimony (Sb), and bismuth (Bi) in primary ores, complicating the production of high-purity copper (>99.99 %). Integrating ion-exchange (IX) polishing stages into electrorefining circuits offers a sustainable approach for selectively removing Sb and Bi, enabling their recovery as Sb₂O₃(s) and Bi₂O₃(s). However, residual arsenic, especially at high concentrations, remains a key obstacle to the direct valorisation of these by-products, requiring further purification. This study investigates several process strategies to reduce arsenic content and improve the purity of the recovered oxides. These strategies include: (i) optimising precipitation conditions across varying Sb/Bi and As molar ratios; (ii) employing iodide as a catalytic reductant; (iii) introducing washing steps to purify intermediate oxychlorides; and (iv) applying chelating agents during the oxide conversion process. Since Sb and Bi are typically recovered in separate industrial processes, their commercial valorisation relies on generating high-purity concentrates suitable for external refining. Following process optimisation, final products exhibited arsenic concentrations below 0.2 % in Sb₂O₃(s) and Bi₂O₃(s) when derived from feeds with high Sb/As and Bi/As molar ratios. For more challenging feed compositions with lower Sb/As and Bi/As ratios, arsenic levels remained below 0.5 % in Sb₂O₃(s) and 1.0 % in Bi₂O₃(s). Although these improvements are significant, impurity levels still exceed the thresholds for most direct-use industrial applications, confirming that additional refining remains necessary. As downstream treatment may reduce product value by up to 50 %, a clear trade-off exists between purity and process complexity that must be carefully considered for industrial application.
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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".