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Record W2048141731 · doi:10.7122/151342-ms

Impact of Cokemaking Technology on a Steel Plant's Carbon Footprint

2012· article· en· W2048141731 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCarbon Management Technology Conference · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsHatch (Canada)Suncor Energy (Canada)
Fundersnot available
KeywordsCarbon footprintFootprintCarbon fibersEnvironmental scienceComputer scienceGreenhouse gasGeology

Abstract

fetched live from OpenAlex

Abstract By-product and heat-recovery cokemaking technologies each offer the steelmaker different opportunities to develop the steelworks' energy balance with the aim to achieve a lower environmental footprint. This paper discusses the results of a Greenhouse Gas (GHG) footprint study completed by Hatch, comparing the GHG emissions of a conventional by-product coke plant with a heat-recovery coke plant within an integrated steel mill. Natural gas and fuel oil were considered as additional fuel sources where coke oven gas was not available. The study followed the Greenhouse Gas Protocol guidelines and reported direct and indirect GHG emissions from the steel plant. The following were the major findings of the study: A steel mill with a heat-recovery coke plant, a blast furnace that used 100% iron ore pellets and where natural gas was used to supplement the steel plant heat balance emitted the lowest total amount of CO2 (1.96 ton CO2/ton HRC). Total GHG emissions from steel mills with heat-recovery coke plants, using natural gas to supplement the steel plant heat balance were lower than those with by-product coke plants in similar steel mill configurations. Total GHG emissions from steel mills with heat-recovery coke plants, using fuel oil, to supplement the steel mill energy balance were also lower than those with comparable steel mills using a by-product coke plant. An integrated steel mill with a by-product coke plant had the lowest Scope 1 GHG emission that represents the emissions from the steel mill site itself. Evaluation of the electricity production as presented in the Scope 2 GHG emissions was essential to understand the complete carbon footprint story as this significantly improved the overall carbon footprint of the steel mill using a heat recovery cokemaking process. INTRODUCTION Iron and steelmaking are fossil fuel energy intensive processes with the global steel industry accounting for between 4% and 5% of total man-made greenhouse gases. The average CO2 intensity for the steel industry is approximately 2.0 tons of CO2 per ton of steel produced. Taking into consideration global steel production of more than 1.3 billion tons, the steel industry produces over two billion tons of CO2 annually. In the fast growing economies of countries such as Brazil, China and India, a major increase in the volume of steel used/produced is anticipated and as a consequence, increased CO2 emissions will result. In conventional steel production, the first step of ironmaking is to carbonize metallurgical coal into blast furnace coke in a coke plant. The resulting coke is charged together with iron ore (pellets and/or sinter) and fluxes (limestone and dolomite) into a blast furnace where iron ore is transformed or smelted into liquid hot metal and slag. The hot metal is then refined to make liquid steel that is cast and rolled into salable products. The main carbon emissions are from the ironmaking processes; cokemaking, sinter/pellet production and blast furnace ironmaking. To foster an energy-efficient process and reduce the carbon footprint, the steel industry improved existing processes and implemented new process technologies with a special focus on the ironmaking area. By-product and heat-recovery cokemaking technologies each offer the steelmaker different opportunities to develop the steelworks' energy balance with the aim of achieving a smaller environmental footprint. This paper compares the GHG emissions of a conventional by-product coke plant with SunCoke's heat-recovery coke plant within an integrated steel mill. Where coke oven gas was not available, natural gas and fuel oil were considered as additional fuel sources. The study followed the Greenhouse Gas Protocol guidelines and reported both direct and indirect GHG emissions from the steel plant. To objectively compare the effect of the cokemaking process on the overall steel mill carbon footprint, a coke plant, blast furnace, BOF-continuous caster and hot strip mill arrangement were considered. Plant arrangements with and without a sinter plant were evaluated. The steel mill capacity used for this study was 3.1 million ton/annum of hot rolled coil (HRC) based on an annual coke production of 1.1 million tons.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.017
GPT teacher head0.264
Teacher spread0.247 · 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