A Review on Pyrometallurgical Extraction of Antimony from Primary Resources: Current Practices and Evolving Processes
Bibliographic record
Abstract
Antimony is classified as a critical/strategic metal. Its primary production is predominated by China via pyrometallurgical routes such as volatilization roasting—reduction smelting or direct reduction smelting. The performance of most of the pyro-processes is very sensitive to concentrate type and grade. Therefore, technology selection for a greenfield plant is a significant and delicate task to maximize the recovery rate of antimony and subsequently precious metals (PMs), mainly gold, from the concentrates. The current paper reviews the conventional pyrometallurgical processes and technologies that have been practiced for the treatment of antimony concentrates. The blast furnace is the most commonly used technology, mainly because of its adaptability to different feeds and grades and a high recovery rate. In addition, several other more environmentally friendly pyrometallurgical routes, that were recently developed, are reviewed but these are still at laboratory- or pilot-scales. For example, decarbonization of antimony production through the replacement of carbonaceous reductants with hydrogen seems to be feasible, although the process is still at its infancy, and further research and development are necessary for its commercialization. At the end, available refining methods for removal of the most important impurities including arsenic, sulfur, lead, iron, and copper from crude antimony are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".