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Record W2770686679 · doi:10.1002/srin.201700363

Effects of Pre‐Reduction Degree of Ironsand on Slag Properties in Melting Separation Process

2017· article· en· W2770686679 on OpenAlexaff
Zhenyang Wang, Jianliang Zhang, Kexin Jiao, Zhengjian Liu

Bibliographic record

Venuesteel research international · 2017
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSlag (welding)Materials scienceSmeltingDegree (music)Softening pointMetallurgySofteningDegree of polymerizationMetalMelting temperatureOxidePolymerizationComposite materialPolymer

Abstract

fetched live from OpenAlex

Ironsand is a kind of titanomagnetite ore and has been maturely used through direct reduction (DR) and melting separation (MS) processes. As an intermediate parameter, pre‐reduction degree (PRD) can be controlled in the upstream DR, and has a considerable influence on metal/slag separation in subsequent MS. Thus, the effects of PRD on the properties of melting separation slag from pre‐reduced ironsand are studied in order to ensure the melting separation sufficient at the condition of slag with high titanium oxide content. The softening, melting, and flowing temperatures of slag show different variation trends along with PRD changing, and the slag at 90% PRD has the narrowest softening‐flowing temperature interval. The viscosity of slag of 90% PRD also shows the proper smelting characteristic. Moreover, Raman spectra results indicate the increase PRD reduces the depolymerized product Si–O–M (metal), which is mainly caused by the decreasing of network‐modifier FeO. Polymerization degree ψ (( Q 3 + Q 2 )/( Q 1 + Q 0 )) illustrates the high PRD is consistent with the complicated silicon network structures, especially when PRD is larger than 90%.

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 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.083
GPT teacher head0.386
Teacher spread0.303 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations12
Published2017
Admission routes1
Has abstractyes

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