Dephosphorization at Low Temperature and Low Basicity in the Double Slag Converter Steelmaking Process With Low CO2 Emission
Bibliographic record
Abstract
To meet the requirements of high efficiency, low cost and environment friendly dephosphorization in the converter steelmaking, the double slag converter steelmaking process (DSP) was developed, in which the typical one is named as Multi-refining converter (MURC) process1, proposed by Nippon Steel, Japan. The process can be divided into the dephosphorization (De-P) stage and decarburization (De-C) stage. In the De-P stage, the desiliconization and dephosphorization are conducted in the converter at first. After intermediate deslagging of the dephosphorization slag, the decarburization is carried out in the same converter. Then the decarburization slag is left in the converter for reusing in the next heat. Due to the recycle of decarburization slag, the lime (mainly CaO) consumption and the waste slag emission can be markedly decreased. According to the report by Sasaki et al., the lime consumption is reduced by 40% compared with the conventional process during the entire converter blowing.2 As lime is produced by the calcination of limestone, in which CO2 is generated as by-product, and the reuse of decarburization slag can also save great amount of the heat energy, DSP can greatly reduce CO2 emissions.
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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".