Energy Efficiency and GHG Emissions for Alternative Iron- and Steelmaking Process Technologies
Bibliographic record
Abstract
Abstract In the changing global market scenario for raw materials for the steel industry, a number of novel iron- and 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. In this context, it is important to assess these critical issues for the alternate iron- and steelmaking technologies that have been developed. This paper presents a comparative evaluation of energy-efficiency and GHG emissions for some selected iron- and 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 CO2 abatement within the iron and steel industry, were employed in the evaluation conducted. INTRODUCTION The iron and steel industry continues to transform itself and evolve in the ever-changing global market place - the raw material scenario is constantly changing with respect to quality and quantity (availability), there is stiff competition in both global and local markets, and there is increasing pressure to address global climate change issues, especially since the steel industry is highly energy- and carbon-intensive. There is growing importance of steel production in developing countries such as China and India - this means that the steel industry in these countries will play an important role in defining and shaping the future of the industry. Climate change is expected to present new risks to the steel industry with respect to ensuring a sustainable business. Legislators are proposing to limit GHG emission by placing an implicit price on CO2 emission - market-based "cap and trade", carbon tax etc. In this scenario, it is important for the steel companies to reduce exposure to climate-related risks and at the same time, find business opportunities within these risks. Thus, there is a need to strategically manage the climate change risks; the key steps to strategically manage climate change risks are presented in Table 1[1]. Some of the steps that are being taken by the steel industry to address climate change risks are presented as follows:Expand usage of current Energy- and CO2-efficient technologies in steel plants to minimize GHG emissions and energy consumptionDevelop novel iron- and steelmaking technological solutions to significantly reduce specific energy consumption and specific GHG emissionOptimize and maximize recycling of steel scrapMaximize value of steel industry by-products (wastes); recycling of steel plant wastesFacilitate use of new generation of steels to improve energy efficiency of steel-using products in partnership with customers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".