Hydrometallurgical strategies for the selective recovery of valuable metals from electric arc furnace dust ( <scp>EAFD</scp> ): A critical review
Bibliographic record
Abstract
Abstract Electric arc furnace dust (EAFD), a hazardous byproduct of steelmaking, is increasingly recognized as a secondary resource for critical metals, including zinc (Zn), lead (Pb), and cadmium (Cd). This critical review examines advancements in the hydrometallurgical processing of EAFD, with a focus on the physicochemical properties of dust, leaching mechanisms, selective complexation, purification techniques, and product recovery. Acidic, alkaline, and complexing agents are compared in terms of efficiency, selectivity, and environmental performance, with sulphuric acid and ammonia‐based systems demonstrating high zinc recovery. Downstream purification methods, such as solvent extraction and electrowinning, are examined in the context of metal separation and sustainability. Economic and environmental assessments highlight the potential for reducing carbon footprint and hazardous waste through optimized hydrometallurgical routes. Current challenges, including reagent recyclability and the management of iron‐rich residues, are critically analyzed, and future research directions are outlined. The review provides a comprehensive framework for advancing EAFD valorization through cleaner, more efficient hydrometallurgical strategies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".