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Record W2106543423

Modeling of Gas flows in Steelmaking Decarburization Processes

2013· article· en· W2106543423 on OpenAlexaboutno aff
Zhili Song

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2013
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsDecarburizationSteelmakingMetallurgyEnvironmental scienceMaterials science
DOInot available

Abstract

fetched live from OpenAlex

The purpose of the current study is to increase the understanding of different steelmaking processes at the decarburization stages by use of mathematical modeling. More specifically, two De-Laval nozzles from a VOD (Vaccum Oxygen Decarburization) process, which is used for producing stainless steels with ultra-low carbon grades, was investigated for different vessel pressures. Moreover, the post combustion phenomena in a BOF or LD (Linz-Donawitz) process as well as an AOD (Argon Oxygen Decarburization) process were studied focusing on the decarburization stage. Two industrial VOD nozzles were numerically studied and compared at different temperatures and ambient pressures. Flow patterns of the oxygen jet under different ambient pressures were predicted and the flow information at different positions from the nozzle was analyzed. In addition, the effects of different ambient temperatures on the jet velocity and the dynamic pressure were compared. The predictions revealed that a little under-expansion is somewhat helpful to improve the dynamic pressure. The jet dynamic pressure and its width for the specific nozzle geometry were also studied. It was observed that a variation in the ambient pressure can influence the jet momentum and its width. In addition, a high ambient temperature was found to have a positive effect on the improvement of the jet dynamic pressure. Furthermore, it was found that a change in ambient pressure has a stronger effect on the jet force than a change in the ambient temperature. In addition, it was proved that the profiles of the dynamic pressure at a certain blowing distance fit well to Multi-Gaussian distribution. Post combustion in a BOF/LD and an AOD process during decarburization was also studied. Two mathematical models were created to show the post combustion phenomenon inside the converters, respectively. For the CFD modeling of the two processes, the realizable k-ɛ model, the species transport model and the discrete ordinate were adopted to calculate the turbulence, gas reaction and radiation present in the gas phase in the converter. For the BOF/LD modeling, a series of plant tests were conducted to collect data, which were used in the current model. These include the off-gas information, emissivity data, oxygen blowing parameters and the chemical composition of steel. After the simulation, the predicted flow pattern and detailed information of the gases taking part in the post combustion were compared to plant data. Specifically, the off-gas data from the plant was used for the model verification. The measured CO2 concentration was 15-20 wt% and the predicted value from the modeling was 16.7 wt%. For the AOD converter of interest in the current work, a fan is installed in the end of the AOD flue to help extract the off-gas from the converter. The influence of different fan gauge pressures as well as temperatures of the gas mixture, containing the generated CO and argon, on the post combustion in the whole AOD system was studied. It was indicated from the modeling results that the post combustion was only present in the flue for the present modeling conditions. Moreover, a critical fan gauge pressure (approx.. -550 Pa) was found which could yield a maximum post combustion in the flue gas. For both two models (BOF/LD and AOD), simulations indicated that a change of the converter temperature from 1500 to 1700 °C did not influence the post combustion reaction to a large degree. In addition, these two models can be regarded as the first step for a future more in-depth modeling work of the post combustion.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.225
Teacher spread0.211 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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