Thermodynamic Investigation of the Sulphation Roasting of Electric Arc Furnace Dust
Bibliographic record
Abstract
During the remelting of automobile scrap in the electric arc furnace steelmaking process, a dust is generated. This dust contains significant amounts of zinc, iron, and lead and in some cases, copper and nickel. However, the recovery of these metals is difficult, because of the complex chemical and physical characteristics of the dust. Numerous pyrometallurgical, hydrometallurgical and hybrid processes have been devised and tested for metal recovery, but only the Waelz rotary kiln process has achieved significant commercialisation. One potential process, which has received little attention in the literature, is the pyrometallurgical sulphation of the dust. In the present research, a high temperature thermodynamic model has been developed using HSC® Chemistry 7.1, to investigate the sulphation of the dust. The effects of process parameters on the conversion of the various metals into sulphates were studied. At a temperature of 600 °C, almost one hundred percent of the zinc could be converted into zinc sulphate, while about ninety-five percent of the iron could be retained as hematite. In addition, several low cost, potential sulphating reagents were evaluated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".