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Record W1568613777

Efficiency in the steel sector

2011· article· en· W1568613777 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBusiness and Public Administration Studies (Washington Institute of China Studies) · 2011
Typearticle
Languageen
FieldEngineering
TopicIron and Steelmaking Processes
Canadian institutionsnot available
Fundersnot available
KeywordsSteelmakingCoalProduction (economics)Blast furnaceEnvironmental scienceIron oreManufacturing sectorEfficient energy useProduct mixWaste managementMetallurgyEngineeringEconomicsMaterials scienceManufacturing engineering
DOInot available

Abstract

fetched live from OpenAlex

The iron and steel sector consumes about 19% of global final energy use and accounts for a quarter of direct CO 2 emissions from industry and roughly 4.5% of global CO 2 emissions (WSA 2008a).Steel production is very energy intensive with 20% to 40% of the cost of steel production derived from energy expenses (WSA 2008a).On average every ton of primary steel produced in a blast furnace results in one-and-a-half to two tons of direct CO 2 emissions in OECD countries (ArcelorMittal 2008).The energy efficiency of steelmaking facilities differ greatly depending on production route, type of iron ore and coal used, the steel product mix, operation control technology, and material efficiency (WSA 2008b).The promise of large CO 2 emission reduction in the steel sector lies in two directions.One is to accelerate the penetration of currently available energy efficiency technologies.The other is to find breakthrough technologies.The best steel mills are now limited by the laws of thermodynamics in how much they can still improve their energy efficiency.For these plants, further large reductions in CO 2 emissions are not possible using current technologies.A portfolio of breakthrough technologies will therefore be required to meet the CO 2 emission standard called for by governments and international institutes (WSA 2008a).Many regional initiatives are being undertaken to identify technologies that hold the promise of large reductions in CO 2 emissions and to explore their feasibility at various scales from lab work, to pilot plant development, and eventually to commercial implementation.The central players include the EU Ultra-low CO 2 Steelmaking Project, 1 the American Iron and Steel Institute, the Canadian Steel Federation, ArcelorMittal Brazil, the Japanese Iron and Steel Federation, the Korean POSCO, China's Baosteel, and Australia's Bluescope (WSA 2008b).Among the portfolio of breakthrough technologies, the coal-based iron-making technologies associated with carbon capture and storage (CCS) technology are the most likely candidates for early maturity.Hydrogen and electrolysis are being explored by the European Union and the United States.Hydrogen could be used as a reducing agent, as its oxidation produces only water.Hydrogen-either pure, as a syngas produced by reforming methane, or as

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.285
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it