Process Optimization and Flowsheet Development for Zinc and Copper Recycling from Reverberatory Furnace Flue Dust
Bibliographic record
Abstract
The copper flue dust is an important secondary source for various metals and also a potential threat to the environment. In this study, a process was developed to convert a local copper flue dust containing 20.60% copper, 21% iron and 2.88% zinc into value-added products via a hydrometallurgical route. The response surface methodology was applied for the optimization of base metals leaching by sulfuric acid. Maximum recoveries of 96% for zinc and 76.7% for copper were achieved under the optimum conditions, whereas only 23.92% of the iron content was dissolved. Moreover, various parameters effective on Zn/Fe separation factor were assessed, and favorable separation obtained at pH 3, 2:1 A/O ratio, 20% V/V of D2EHPA in kerosene during 15 minutes. Stripping experiments also showed that 96.35% of zinc was successfully stripped at 1 M sulfuric acid and 2:1 A/O. The mathematical prediction models for leaching, solvent extraction and stripping were proposed and confirmed by statistical analysis and experiments. The proposed process in this study, enhances the copper production in the current leaching plant and makes it possible to recover zinc from industrial waste as a by-product.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".