Analysis of the possibility of replacing carbon with hydrogen in the conditions of smelting cast iron from vanadium-containing titanomagnetites
Bibliographic record
Abstract
75 % of the total carbon dioxide emissions by ferrous metallurgy enterprises is generated in the blast furnace process. One of the directions of CO2 emission reduction in pig iron production is partial replacement of carbon monoxide with hydrogen as a reducing agent. It is shown that such a replacement can lead to a decrease in the total carbon consumption due to reduction of heat consumption for the direct reduction of iron oxides. Using a mathematical model of the blast furnace process, the efficiency of partial replacement of process fuel (coke, natural gas, pulverized coal) with a hydrogen additive was evaluated. Calculations were performed for the operating conditions of blast furnaces of EVRAZ NTMK JSC, which melt vanadium-containing titanomagnetites. The coefficients of process fuel replacement with hydrogen and the coefficients of the influence of the replacement of process fuel with hydrogen on the change in CO2 emissions are calculated. The dependence of the change in the productivity of the furnace on the consumption of hydrogen at a constant minute flow rate of the blast and its adjustment to maintain the pressure drop has been established. It is shown that in the absence of gas dynamics reserves, the replacement of process fuel with hydrogen will be accompanied by a decrease in furnace productivity. The smelting of pig iron from titanomagnetites with the replacement of technological fuel with hydrogen will complicate the refining of melting products due to an increase in the formation of titanium carbides and carbonitrides. The reduction in CO2 formation at hydrogen entering the blast furnace with natural gas is 0.35 kg/m3 compared to pure hydrogen replacement of 0.73 kg/m3, not taking into account CO2 emission during hydrogen production
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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 | 0.001 |
| 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".