CFD Modelling of Refining Behaviour in EAF: Influence of Burner Arrangement and Oxygen Flow Rates
Bibliographic record
Abstract
The electric arc furnace (EAF) process includes key stages: charging scrap metal, melting using electric arcs, refining through oxygen injection and slag formation, and tapping molten steel. Recently, EAF steelmaking has become increasingly important due to its flexibility with recycled materials, lower environmental impact, and reduced investment costs. This study focuses specifically on select aspects of the refining stage, analysing decarburization and the associated exothermic oxidation reactions following the removal of carbon with oxygen injection. Particular attention is given to FeO generation during refining, as it strongly affects slag chemistry, yield losses, and overall efficiency. Using a Computational Fluid Dynamics (CFD)-based refining simulator validated with industrial data from EVRAZ North America (showing an 8.57% deviation), this study investigated the impact of oxygen injection rate and burner configuration. The results in a three-burner EAF operation showed that increasing oxygen injection by 10% improved carbon removal by 5%, but with an associated increase of FeO generation of 22%. Conversely, reducing oxygen injection by 15% raised the residual carbon content by 43% but lowered FeO by 23%. Moreover, the impact of the number of burners was analysed by simulating a second scenario with 6 burners. The results show that by increasing the number of burners from three to six, the target carbon is reached 33% faster while increasing FeO by 42.5%. Moreover, by reducing the oxygen injection in the six-burner case, it is possible to reduce FeO generation from 42.5 to 28.5% without significantly impacting carbon removal. This set of results provides guidance for burner optimization and understanding the impact of oxygen injection on refining efficiency.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".