Using a Novel Scaled Injector to Evaluate Biocarbon for Slag Foaming in EAF Steelmaking
Bibliographic record
Abstract
Abstract The steel industry faces increasing pressure to reduce carbon emissions, driving interest in sustainable alternatives to fossil-based carbon materials. Biocarbon, derived from renewable biomass, has shown promise as a charge carbon in electric steelmaking, but research into its application as an injection carbon for slag foaming is still ongoing. While studies have examined the reactivity and slag foaming behavior of biocarbon, its injectability has not been adequately evaluated due to the lack of a suitable experimental equipment. This study addresses this gap with a novel setup using a laboratory induction furnace and featuring a specially designed scaled-down industrial injector. Unlike previous experiments, which focused on evaluating slag foaming behaviour, this configuration enables the assessment of biocarbon injectability and slag foaming initiation under controlled laboratory conditions. CFD simulations of biocarbon injection helped inform both the injector design and the experimental conditions, ensuring effective penetration of the biocarbon particles. By closely replicating industrial injection conditions, this setup facilitates a new understanding of biocarbon’s potential as a sustainable alternative in electric steelmaking.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".