The modern technology of iron and steel production and possible ways of their development
Bibliographic record
Abstract
In the changing global market scenario for raw materials for the steel industry, a number of novel ironand steelmaking process technologies are being developed to provide the steel companies with economically-sustainable alternatives for ironand steel-making. In addition, the steel industry is also focusing on reduction of energy consumption as well as green-house gas (GHG) emissions to address the crucial subject of climate change. Climate change is presenting new risks to the highly energyand carbon-intensive, iron and steel industry. The industry needs to focus on reduction of energy consumption as GHG emissions to address climate change. Development of alternate ironand steelmaking process technologies can provide steel companies with economically-sustainable alternatives for steel production. For managing climate change risks, novel modeling tools have been developed by Hatch to quantify and qualify potential energy savings and CO 2 abatement within the iron and steel industry. The tool developed for abatement of greenhouse gas carbon is called G-CAPTM (Green-House Gas Carbon Abatement Process) while that developed for improving energy efficiency is called En-MAPTM (Energy Management Action Planning). Evaluation of existing operations have shown that most integrated plants have GHG and energy abatement opportunities; on the other hand, the best-in-class plants may not have a lot of low-risk abatement opportunities left, even at high CO 2 price. In this context, it is important to assess these critical issues for the alternate ironand steelmaking technologies that have been developed. This paper presents a comparative evaluation of energy-efficiency and GHG emissions for some selected ironand steelmaking technologies that are being considered for implementation. In this work, Hatch’s G-CAP™ and En-MAP™ tools that were developed with the main objective of quantifying and qualifying the potential energy savings and CO 2 abatement within the iron and steel industry, were employed in the evaluation conducted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".