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Optimization of agglomeration burdens by metallugical properties complex. Report 1. Optimization of agglomeration burdens by technological characteristics of sinter production

2018· article· en· W2905201751 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

VenueFerrous Metallurgy Bulletin of Scientific Technical and Economic Information · 2018
Typearticle
Languageen
FieldEngineering
TopicIron and Steelmaking Processes
Canadian institutionsArcelorMittal (Canada)
Fundersnot available
KeywordsEconomies of agglomerationSinteringProductivityLimeRaw materialMetallurgyMaterials scienceProcess engineeringEnvironmental scienceEconomicsEngineering

Abstract

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Despite a lot of studies of iron ore raw materials was carried out both in sinter and BF production areas, the matter of agglomeration burdens optimization is still actual. Laboratory studies on sintering of agglomeration burdens of different component content were carried out for optimization of iron ore burden content optimization, following by determination of technology characteristics and metallurgical properties complex. As a result of the studies an optimal component and size content of the agglomeration burden determined to provide improving of metallurgical properties complex of agglomeration burden. The studies carried out showed, that lime introducing into the concentrate flow before the burden department can lead to sintering machines productivity increasing. The burden wetness range determined, enabling for complete lime hydrating. It was shown, that a partial replacement of agglomeration ores in the burden by BOF nickel slag contributes to agglomeration process specific productivity increasing as well as sinter strength increasing.

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)0.001
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 score0.271
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.012
GPT teacher head0.196
Teacher spread0.184 · 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