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Record W4365454977 · doi:10.1002/srin.202300081

Numerical Study on the Effect of Low‐Frequency Power Supply on Desulfurization in the Electroslag Remelting Process

2023· article· en· W4365454977 on OpenAlexaff
Yiru Duan, Baokuan Li, Zhongqiu Liu, Xuechi Huang, Donghui Li, Fang Wang

Bibliographic record

Venuesteel research international · 2023
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsFlue-gas desulfurizationMaterials scienceElectrodeCurrent (fluid)Slag (welding)Power (physics)MechanicsVolumetric flow rateMetallurgyChemistryThermodynamicsWaste managementElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

In the electroslag remelting (ESR) process, low‐frequency power supply can significantly reduce power consumption and achieve three‐phase balance of power supply. Therefore, a transient coupling model of fluid flow, heat transfer, and component transport in the ESR process, which is coupled to the electromagnetic field calculated using Maxwell 3D software, is established to study the influence of low‐frequency power supply on desulfurization. When a 50 Hz power supply is used, a skin effect is observed in the metal, and the direction of the Lorentz force at the slag/metal interface changes. However, this effect becomes less pronounced with decreasing current frequency. Sulfur is mainly transferred at the electrode tip, and the desulfurization rate is approximately 50%. Electrochemical reactions mainly occur at the electrode tip/slag interface and the metal pool/slag interface. The removal rate of sulfur using direct current (DC) power supply is less than that using an alternating current power supply. The DC reverse polarity power supply leads to higher desulfurization rate than DC straight polarity, which is 74% and 31%, respectively. The sulfur removal rate increases from 81.37% to 84.59% as the frequency decreases from 50 to 2 Hz because of the longer electrochemical reaction time at this lower frequency.

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)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.361
Teacher spread0.329 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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