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Record W4399057695 · doi:10.1177/03019233231215953

Influence of oxygen enrichment method on the state of blast furnace raceway

2024· article· en· W4399057695 on OpenAlexaff
Kai Wang, Yining Huang, Jianliang Zhang, Shengli Wu, Cuiliu Zhang, Jian Chen, Lian Ye, Zhaojie Teng, Runsheng Xu

Bibliographic record

VenueIronmaking & Steelmaking Processes Products and Applications · 2024
Typearticle
Languageen
FieldEngineering
TopicIron and Steelmaking Processes
Canadian institutionsWestern University
Fundersnot available
KeywordsRacewayBlast furnaceMetallurgyPig ironOxygenMaterials scienceEngineeringWaste managementForensic engineeringNuclear engineeringChemistryMechanical engineering

Abstract

fetched live from OpenAlex

The numerical simulation is applied to compare the effect of the oxygen enrichment rates under different injection methods on the flow and combustion features. The alteration in the gas velocity follows a congruous pattern for constant air and oxygen enrichment (CAOE) method and the reduced air and oxygen enrichment (RAOE) method. The oxygen enrichment rate increases by 1% and the velocity at the tuyere increases by 2.92 and 0.78 m s −1 . The CAOE method has a more significant influence on the temperature and burnout of coal particles than that of the RAOE method. Furthermore, the burnout of coal particles at the exit of the raceway is 79.45% and 78.95%, at an oxygen enrichment rate of 12% under the conditions of the CAOE method and RAOE method. To improve the penetration capacity of the hearth and achieve higher burnout of pulverised coal, the CAOE method is recommended to enrich oxygen.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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