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Record W2078427738 · doi:10.1115/ht2008-56363

Methodology for the Numerical Simulation of Natural Gas, Coal, and Coke Combustion in a Blast Furnace

2008· article· en· W2078427738 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIron and Steelmaking Processes
Canadian institutionsUnion Gas (Canada)
Fundersnot available
KeywordsBlast furnacePulverized coal-fired boilerBlast furnace gasCokeTuyereCoalCombustionNatural gasMetallurgyIron oreWaste managementMaterials scienceRacewayCoal combustion productsGround granulated blast-furnace slagPig ironEnvironmental scienceEngineeringChemistryComposite materialCement

Abstract

fetched live from OpenAlex

A blast furnace is a reaction vessel in which iron ore is converted to molten iron. High rate pulverized coal injection (PCI) into a blast furnace (BF) is an existing process that is known to decrease the amount of coke in the ironmaking process. Natural gas co-injection with pulverized coal increases the burnout and devolatilization rates of pulverized coal. Also, hydrogen produced from natural gas combustion is a powerful reducing agent of iron (III) oxide, releasing pure iron that trickles down and is eventually removed through the taphole. Due to the inherent complexity of the blast furnace ironmaking process, numerical simulation can prove to be quite difficult. This paper describes a three step methodology for modeling blast furnace combustion, and its application to a furnace in operation at USSC Hamilton Works.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.666
Threshold uncertainty score0.129

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.062
GPT teacher head0.303
Teacher spread0.241 · 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

Quick stats

Citations3
Published2008
Admission routes1
Has abstractyes

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