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Record W2791365955 · doi:10.1002/srin.201700450

The Effect of Sulfur Concentration in the Metal on the Mass Transfer of Phosphorus in Bloated Metal Droplets

2018· article· en· W2791365955 on OpenAlexafffund
Kezhuan Gu, Neslihan Dogan, Kenneth S. Coley

Bibliographic record

Venuesteel research international · 2018
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsSulfurMass transferChemistryOxidizing agentOxygenMetalDecarburizationSwellingAnalytical Chemistry (journal)Materials scienceComposite materialChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

In Basic Oxygen Steelmaking, metal droplets created by the impact of the oxygen jet will swell due to internal decarburization when they react with oxidizing slag in the emulsion zone. In this study, the dephosphorization behavior of bloated metal droplets containing different sulfur concentrations is investigated in the temperature range from 1813 to 1913 K using X‐ray fluoroscopy coupled with constant volume pressure change measurements and chemical analysis of quenched samples. The results show that higher sulfur droplets (>0.017 wt%) have a much longer incubation period for swelling than those with lower sulfur (<0.017 wt%). Due to this different swelling behavior, the dephosphorization rate for high sulfur droplets is found to be not heavily affected by the experimental temperature compared to the case of lower sulfur droplets. Employing a mixed control model including mass transfer and chemical reaction, the effect of temperature on dephosphorization kinetics is further discussed. The mass transfer coefficient in the metal, k m , is found to increase slightly with increasing temperature for droplets with 0.021 wt% sulfur. It is also found that lower rates of CO formation result in less stirring in the droplet and lower mass transfer rates of phosphorus for high sulfur droplets.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.731
Threshold uncertainty score0.160

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
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 head0.026
GPT teacher head0.312
Teacher spread0.286 · 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

Citations9
Published2018
Admission routes2
Has abstractyes

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