Coating of Biocarbon to Reduce Reactivity for Slag Foaming Applications in Electric Arc Furnace Steelmaking
Bibliographic record
Abstract
Abstract As the steel industry transitions toward net-zero greenhouse gas emissions, biocarbon emerges as a promising renewable alternative to replace fossil carbon for slag foaming in electric arc furnace (EAF) steelmaking. However, the high porosity and reactivity of biocarbon leads to technical challenges associated with injection of biocarbon and foam stability, which reduces process energy efficiency. This study investigates a novel approach to address the technical challenges by enhancing biocarbon performance in slag foaming. The enhancement is achieved by coating solid biocarbon particle with bio-oil followed by heat treatment to reduce particle porosity and reactivity. Petcoke, uncoated biocarbon, and bio-oil-coated biocarbon were systematically characterized to evaluate their physicochemical properties, reactivity profiles, and interaction with synthetic slag. Particle morphology analysis revealed that coating reduced biocarbon porosity and increased biocarbon surface roughness. Thermogravimetric analysis (TGA) experiments confirmed that coating moderated biocarbon reactivity with air and CO₂, and slag. Interaction tests with slag revealed that coated biocarbon exhibited intermediate behavior, although still more reactive than petcoke but much less reactive than uncoated biocarbon, facilitating more stable and prolonged slag foaming. Coated biocarbon can possibly generate sustained foamy slag with improved duration compared to uncoated biocarbon, while still achieving comparable foaming height. These findings highlight the potential of coated biocarbon overcome the technical barrier of biocarbon utilization and serve as a feasible, low carbon-intensive injection material for EAF steelmaking process. Graphical Abstract
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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 | 0.000 | 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 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".