Chemical energy and bottom stirring systems – Cost effective solutions for a better performing EAF
Bibliographic record
Abstract
The continuous improvement of EAF performance is crucial for the increase in productivity and cost effectiveness and to render an environmental friendly process. RHI supports these targets by providing a diverse range of refractory and process solutions. Chemical energy in practical operation by injection of gaseous media like oxygen and natural gas, and solid materials like carbon have become a state of the art solutions for process improvement today. To understand the phenomena of the benefits, RHI has developed a mass and energy balance model. The objective of the model is to focus on the replacement ratio of electrical energy by chemical energy and the consequences for the overall CO2 emission of the process. CO2 emission certificate trading can become a new source for cash generation to be utilized for improvement investment budgets. Another valuable solution to decrease the energy consumption and the CO2 generation of the process is bottom purging in EAF. This technology can be introduced by the use of the directional porosity plug (DPP). The benefit of stirring in the furnace is that it enhances the interface reactions between the slag and the bath and homogenizes the liquid volume in the furnace. This results in achieving better operation parameters in terms of less power consumption and power-on time. Again a benefit in cost and a reduction in carbon dioxide emissions can obviously be attained. This paper shows the savings potential of the replacement of electrical energy by chemical energy by using the mentioned balance model. The benefit of the introduction of the DPP system is shown on the results of various steel plants.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".