Optimization of direct reduction in tunnel furnace using different resources of ferrous oxides
Bibliographic record
Abstract
Nowadays it is highly desired to maximize using of existing resource and recycle waste materials. The by-product of steel being hot rolled is mill scale that disposing of it as waste material has environmental effects. Therefore, the use of mill scale in iron production is economically and environmentally beneficial. In the current work, an attempt has been made to use mill scale and iron concentrate which are not applicable to pelletized, in the reduction process with tunnel kiln for iron manufacturing. Non-coking coal and limestone were utilized as reducing agents. The reluctant to ferrous oxide ratio was kept constant during the reduction tests. The reduction process was carried out in a crucible at 1150 °C. The analyses of the metal Fe content in the reduced samples show that the mill scale can be used successfully in the direct reduction process to produce sponge iron. In the rolling mill scale-iron pellet, iron concentrate-iron pellet, and iron concentrate-mill scale mixtures, the compositions 70MS-30IP, 70IC-30IP, and 70IC-30MS were optimum. The result of XRD and STA results revealed that the optimal heat treatment setting for reducing utilized ferrous oxide mixtures is 1150 °C for 1 h.
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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) | 0.001 | 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".