Analysis of hot metal desulfurization methods efficiency during titanomagnetite processing
Bibliographic record
Abstract
To make high-quality steel, it is necessary to ensure low sulfur content in the hot metal entering a steelmaking shop. The task to obtain low sulfur metal can be solved either during blast furnace smelting or its ladle desulfurization immediately after tapping out of blast furnace. Under conditions of JSC EVRAZ NTMK, the ladle desulfurization of vanadium hot metal is not applied, since it leads to a loss of vanadium. Is explained by the fat, that calcium oxide forms with V 2 O 5 a strong calcium vanadate (3СаО·V 2 O 5 ), which makes the process of vanadium recovery during further slag processing more complicated. Therefore the ladle desulfurization is accomplished after devanadation of hot metal. After the devanadation the semi-product has ions of oxygen, which makes it impossible to arrange an effective desulfurization and males it necessary to smelt the hot metal with limited sulfur content. The factors, effecting the process of hot metal desulfurization in a blast furnace, including slag basicity, MgO content in it, temperature of smelting products and slag viscosity, are presented. Dependence of distribution coefficient of sulfur on slag basicity has been shown. Research goal is pig iron desulphurization efficiency estimation by means of MgO increasing in slag. Data analysis findings about smelting products composition over 500 tappings for each of two investigated JSC EVRAZ NTMK blast furnaces have been reported. The aim of the study was comparison of hot metal desulphurization efficiency due to both slag basicity increase and MgO increase in slag. Results of data analysis on smelting products composition of over 500 heats for each of two investigated JSC EVRAZ NTMK blast furnaces have been presented. It was established that a sulfur content decrease in hot metal is achieved more effectively by MgO increase in slag. It has been shown that MgO has better desulfurizing properties, and with an increase of its content in the blast furnace slag, the overall technology for running the blast furnace process improves. It was recommended to increase the consumption of materials containing MgO while maintaining basicity at a constant level. Such a technology will allow to decrease sulfur content in hot metal, as well as will result in the process stabilization providing other conditions being equal.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".