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Record W2273343032 · doi:10.25916/sut.26285800

Nitrogen removal from steel by DRI fines injection

2005· article· en· W2273343032 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

VenueSwinburne Research Bank (Swinburne University of Technology) · 2005
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNitrogenWaste managementMetallurgyWork (physics)Carbon fibersEnvironmental scienceCarbon steelMaterials scienceChemistryEngineeringComposite materialMechanical engineeringCorrosion

Abstract

fetched live from OpenAlex

Nitrogen even in small quantities is detrimental to the quality of steel, and it is difficult to remove from steel. The goal of this work was to develop a technique for nitrogen removal from liquid steel by injection of DRI fines. DRI fines, generated either directly from a DRI process or by attrition in transport or handling, contain significant quantities of carbon and oxygen. Studies have shown that upon heating these elements react rapidly inside DRI particles to form fine CO bubbles. The present study examines the generation of CO by the injection of DRI fines; these fines are generated inevitably from the handling and transportation of DRI pellets and briquettes, but such fines could be specifically prepared by crushing DRI pellets. It should be noted that some EAF shops already inject DRI fines to gain some value from this degraded product. To our knowledge there have been no studies to isolate the effect of fines injection, and to optimize this process for nitrogen removal.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
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 science0.0010.000
Research integrity0.0010.001
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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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