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Enregistrement W2048141731 · doi:10.7122/151342-ms

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

2012· article· en· W2048141731 sur OpenAlexaff
Chris Sharp, Yakov Gordon, Sining Liu, Paul Towsey, Ian Cameron

Notice bibliographique

RevueCarbon Management Technology Conference · 2012
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueRecycling and Waste Management Techniques
Établissements canadiensHatch (Canada)Suncor Energy (Canada)
Organismes subventionnairesnon disponible
Mots-clésCarbon footprintFootprintCarbon fibersEnvironmental scienceComputer scienceGreenhouse gasGeology

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,016

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,264
Écart entre enseignants0,247 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2012
Routes d'admission1
Résumé présentoui

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