Evaluation of Biochar and Coke Blends for Slag Foaming Applications in Electric Arc Furnace Steelmaking
Bibliographic record
Abstract
The electric arc furnace (EAF) is a promising approach to decarbonize the iron and steel industry. In EAF steelmaking, injecting carbon into the molten slag remains crucial for creating a foamy slag, which enhances the energy efficiency of the process and protects the furnace. Biochar (BC) has emerged as a potential alternative to traditional fossil carbon for slag foaming. However, fully replacing fossil carbon with BC poses technical challenges. In this study, the partial replacement of fossil carbon with BC is considered, in the form of a petroleum coke (petcoke) and BC blend. Interestingly, a blend of petcoke and BC matches or possibly outperforms either carbon type individually, due to a synergistic effect. Using an induction furnace to simulate EAF conditions, a synthetic slag is melted, and injection carbon is added into the slag layer. The slag foaming effectiveness of petcoke, BC, and three blend cases are studied. Thermogravimetric analysis reveals that the BC is more reactive with slag compared to petcoke, which leads to an initial high intensity of CO generation. However, the CO generation was not continuous or consistent. All experimental results are combined to propose a mechanistic description of the slag foaming behavior of BC blends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 teacher head, 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".