Physicochemical properties of steelmaking slags for the mitigation of CO2 emissions in steel sector
Bibliographic record
Abstract
In this paper, the challenging points regarding the high temperature physical chemistry of slags to achieve the improved and stable electric arc furnace (EAF) or electric smelting furnace (ESF) technology on the way to green steel will be reviewed, and the recent experimental and modelling research will be discussed. For example, the initial melting phenomena of hot briquetted iron (HBI) and the slag formation behaviour was observed using a high-frequency induction furnace. Main component of gangue oxides in HBI was SiO2, Al2O3, and CaO in conjunction with unreduced iron oxide. To increase the dephosphorisation efficiency, the distribution ratio of phosphorus between metal and slag was calculated using FactSage™ software, version 8.2 (CRCT ThermFact, Inc., Montreal, Canada) and was compared to the measured results. The optimisation of slag chemistry is required not only for maximum dephosphorisation efficiency with good slag foamability but also for minimum slag volume with less refractory corrosion in EAF process. The slag chemistry is also one of key parameters affecting the operation efficiency in ESF in view of FeO reduction behaviour, viscosity, sulfide capacity, etc.
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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 | 0.001 |
| 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 | 0.001 | 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".