MétaCan
Menu
Retour à la cohorte
Enregistrement W2106543423

Modeling of Gas flows in Steelmaking Decarburization Processes

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

Notice bibliographique

RevueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2013
Typearticle
Langueen
DomaineEngineering
ThématiqueMetallurgical Processes and Thermodynamics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDecarburizationSteelmakingMetallurgyEnvironmental scienceMaterials science
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,352
Score d'incertitude au seuil0,749

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,225
Écart entre enseignants0,211 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2013
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueKTH Publication Database DiVA (KTH Royal Institute of Technology)Même sujetMetallurgical Processes and ThermodynamicsTravaux en français237 207