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Record W4378087468 · doi:10.3390/met13061003

Ranking of Injection Biochar for Slag Foaming Applications in Steelmaking

2023· article· en· W4378087468 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMetals · 2023
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsNatural Resources Canada
FundersOffice of Energy Research and DevelopmentArcelorMittal
KeywordsSteelmakingSlag (welding)BiocharBriquetteElectric arc furnaceCarbon fibersMaterials scienceMetallurgyWaste managementBasic oxygen steelmakingPelletEnvironmental scienceCoalComposite materialEngineeringPyrolysis

Abstract

fetched live from OpenAlex

The electric arc furnace (EAF) has the potential to significantly contribute to the decarbonization of the iron and steel industry. However, during EAF steelmaking, carbon still needs to be injected into the molten slag to initiate slag foaming, which is beneficial to the energy efficiency and protection of the furnace. To move away from fossil carbon, biocarbon has gained attention as an injection carbon agent. In this study, two biochar candidates were added to the molten slag layer of an induction furnace for steel melting, to simulate EAF steelmaking conditions. The resultant slag foaming height was measured, and a ranking in comparison to two fossil carbon candidates was developed. The results indicate that the injection biochar sample, in the form of a bio-briquette, has a considerable degree of slag foaming capacity. More work is ongoing to develop a standardized testing methodology of ranking various injection biochar candidates for their suitability and qualification for use on a larger scale.

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: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.264

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 head0.020
GPT teacher head0.261
Teacher spread0.240 · 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