Sustainable Processing of Liquid Steel and Alloys Using Waste Material from the Aluminum Industry
Bibliographic record
Abstract
Waste slag is a major source of environmental pollution within the metallurgical industry. In the steel industry, about 100-150 kg of waste slag is produced in making one tonne of liquid steel from iron in addition to that produced during ironmaking. The amount of waste slag from the steelmaking converter can be significantly reduced when charging hot metal with low phosphorus and sulphur contents. There is a great interest in searching for fluxes for hot metal pre-treatment and liquid steel refining which produce high efficiency, low cost and environmentally friendly processes. In the aluminium industry, about 2-4 tonnes of waste slag, including by-products (red mud and white mud) from alumina production and dross from aluminum electrolysis and casting, are generated during the production of one tonne of aluminum. Red mud is the by-product from alumina production and it is the largest environmental concern of alumina refineries mainly because of the size of this waste stream and its causticity. In this context, the research carried out at the University of Toronto associated with the reuse of waste materials or by-products from the aluminum industry as refining fluxes in the steel industry will be summarized. Topics discussed include: (1) Reuse of white mud generated during aluminum melting and refining for hot metal desulphurization; (2) Using fluxes produced from red mud for hot metal simultaneous dephosphorization and desulphurization, and (3) Removal of phosphorus, sulphur and arsenic from nickel-based alloy generated during the treatment of used catalysts from the petroleum industry. Promising results were achieved in each of these research areas. Sustainable processing of waste materials or by-products from one industrial sector can provide economical refining materials for other industrial sectors and generate environmental and social benefits for both sectors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".