Energy efficient technology of hot metal smelting at PAO NLMK
Bibliographic record
Abstract
Decrease of energy expenditure for hot metal smelting is an actual task in blast furnace technology perfection. Under conditions of PAO NLMK regimes with maximum forcing of the BF process at the expense of pressure increase under furnace mouth and coke hot strength increase were chosen as one of priority ways to increase the BF operation energy efficiency. Data on blast furnaces productivity, specific coke rate, quantity of blow-in oxygen and fuel at different gas pressure levels under the furnace mouth quoted. A dependence between pulverized coal rate and total carbon consumption determined. It was shown, that application in PAO NLMK blast furnaces of coke having hot strength of 60–65% at maximum possible pressure under the furnace mouth and application of pulverized-coal fuel enabled in the period from 2012 through 2018 to decrease the coke rate by more than 100 kg. Also the total carbon consumption decreased by more than 30 kg per hot metal ton and to increase smelting products chemical composition stability.
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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) | 0.002 | 0.001 |
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".