Applicability of a New Binder for Ferro-coke Focusing on the Permeation Behavior
Bibliographic record
Abstract
Ferro-coke, which is produced by mixing coal and iron ore, briquetting and carbonizing, can be used in a blast furnace to greatly decrease the reducing agent ratio. To ensure the strength of ferro-coke, asphalt pitch (ASP) is used as a binder, but the supply of ASP is limited, and the development of alternative binder are required. This study investigated the application of Hyper-coal (HPC), a low-ash caking additive obtained by solvent extraction of coal, as a new binder for ferro-coke. It was found that superior ferro-coke strength could be obtained by using HPC in which insoluble solid concentration was less than 15 wt.%, to that of ASP. This threshold value was specified from the permeation tests. The permeabilities of binders were determined by measuring the permeation distance in the packed layer of coal and/or iron ore under the carbonizing conditions. HPC appeared higher permeability than ASP in the packed layer of iron ore and coal mixtures. It was considered that the excellent thermal plasticity of HPC, lower melting temperature and higher fluidity than ASP, affected higher permeation into the inter particle void especially lower temperature range before starting the reduction of iron ore, which rapidly decreased in the permeabilities of both binders due to the distortion of carbon structures. Those results suggested that HPC was superior to ASP as a binder for ferro-coke.
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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 | 0.001 |
| 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".