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Relating to the rational utilization of manganese-containing raw materials

2019· article· en· W2913904842 on OpenAlexaff
О. И. Нохрина, И. Д. Рожихина, I. E. Proshunin, М. А. Голодова, I. E. Khodosov

Bibliographic record

VenueFerrous Metallurgy Bulletin of Scientific Technical and Economic Information · 2019
Typearticle
Languageen
FieldEngineering
TopicEngineering and Environmental Studies
Canadian institutionsEVRAZ (Canada)
Fundersnot available
KeywordsManganeseBeneficiationRaw materialMetallurgyNickelSmeltingMaterials scienceFlow chartChemistryEngineering

Abstract

fetched live from OpenAlex

Data on main manganese ores deposits by Russian Federation subjects presented. It was shown, that main part of manganese ore raw materials prognostic resources are concentrated in Altaj-Sayan and Enisej-East-Sayan metallogenic provinces. Estimation of metallurgical value of manganese ores deposits, located at the territory of Altaj-Sayan metallogenic province, carried out. A technological flow-chart of manganese-containing raw materials elaborated, comprising high quality manganese concentrate obtaining, its preparation, synthesis of marokite and mono-phase CaMnO3 material, marokite briquetting with a reducing agent and application for steel processing in ladle-furnace facility. A possibility shown to utilization of CaMnO3 mono-phase material mixed with a reducing agent and high quality manganese concentrate for production of metal manganese. Thermodynamic calculations and experiment studies on polymetallic manganese-containing raw material beneficiation enabled to determine main technological parameters of extraction and elaborate a technological flow-chart of beneficiation. The elaborated technology enables to obtain high quality manganese, nickel, iron and cobalt concentrates. Application of optimal technological parameters of beneficiation enables to extract from a polymetallic manganese-containing raw materials up to 95–97% of manganese, 98–99% of nickel, 96–98% of iron. It was shown, that it is reasonable to use the manganese concentrate for low phosphor metal manganese smelting, that will enable to decrease the dependence from manganese-containing materials import. A technology of steel alloying by obtained nickel concentrate elaborated. The substitution of metal nickel by nickel concentrate will considerably reduce expenses for alloying. A technology of metalized iron production by a solid-phase reducing method from an iron concentrate also elaborated, which will enable to decrease impurities content in steel during its application.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.180
Teacher spread0.171 · 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 designTheoretical or conceptual
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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