Hydrometallurgical Leaching of Copper Flash Furnace Electrostatic Precipitator Dust for the Separation of Copper from Bismuth and Arsenic
Bibliographic record
Abstract
Flash furnace electrostatic precipitator dust (FF-ESP dust) is a recycle stream in some primary copper production facilities. This dust contains high amounts of copper. In some cases, the FF-ESP dust contains elevated levels of bismuth and arsenic, both of which cause problems during the electrorefining stages of copper production. Because of this, methods for separation of copper from bismuth and arsenic in FF-ESP dust are necessary. Hydrometallurgical leaching using a number of lixiviants, including sulfuric acid, sulfurous acid, sodium hydroxide, and water, were explored. Pourbaix diagrams of copper, bismuth, and arsenic were used to determine sets of conditions which would thermodynamically separate copper from bismuth and arsenic. The data indicate that water provides the best overall separation between copper and both bismuth and arsenic. Sodium hydroxide provided a separation between copper and arsenic. Sulfurous acid provided a separation between copper and bismuth. Sulfuric acid did not provide any separations between copper and bismuth or copper and arsenic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".