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Metals
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Process and Numerical Simulation of Oxygen Steelmaking
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Process and Numerical Simulation of Oxygen Steelmaking

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 11922

Special Issue Editors

Dr. Mingming Li

E-Mail Website
Guest Editor
School of Metallurgy, Northeastern University, Shenyang, China
Interests: modelling and simulation of transport phenomena in process metallurgy; oxygen and electric steelmaking; low-carbon technology in steelmaking
Dr. Guangsheng Wei

E-Mail Website
Guest Editor
School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: EAF steelmaking; CO2 utilization; injection metallurgy; low-carbon metallurgy
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Chen

E-Mail Website
Guest Editor
College of Materials Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Interests: process modeling; clean steel; inclusions; CFD; physical model; tundish; ladle refining; stainless steel
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Despite the fact that oxygen steelmaking is a well-established and dominant steelmaking technology for many years, it is difficult to fully understand this process due to the harsh environment and the extremely complicated physical and chemical phenomena which occur in the process (such as supersonic oxygen jet phenomena), its physical and chemical interactions with molten metal and slag, as well as the melting of scrap and flux, etc. This challenge also involves more precisely controlling and operating the oxygen steelmaking process.

The present topic intends to promote research and understanding in the fields of process phenomena, as well as optimization and control of oxygen steelmaking, by employing conventional and innovative technology. The aim of the Special Issue is to present the latest achievements of theoretical, experimental, and numerical investigations, and to provide updates on the state of the art in this regard, providing readers with useful information on recent technologies. It is our pleasure to invite you to submit a manuscript to this Special Issue. Full papers, short communications, and reviews are all welcome for submission.

Dr. Mingming Li
Dr. Guangsheng Wei
Dr. Chao Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • novel oxygen steelmaking technologies
  • process control and optimization
  • intelligent control technology and model development
  • numerical simulation
  • physical modelling

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Published Papers (7 papers)

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Research

24 pages, 4720 KiB  
Article
Multi-Output Prediction Model for Basic Oxygen Furnace Steelmaking Based on the Fusion of Deep Convolution and Attention Mechanisms
by Qianqian Dong, Min Li, Shuaijie Hu, Yan Yu and Maoqiang Gu
Metals 2024, 14(7), 773; https://doi.org/10.3390/met14070773 - 29 Jun 2024
Viewed by 1093
Abstract
The objective of basic oxygen furnace (BOF) steelmaking is to achieve molten steel with final carbon content, temperature, and phosphorus content meeting the requirements. Accurate prediction of the above properties is crucial for end-point control in BOF steelmaking. Traditional prediction models typically use [...] Read more.
The objective of basic oxygen furnace (BOF) steelmaking is to achieve molten steel with final carbon content, temperature, and phosphorus content meeting the requirements. Accurate prediction of the above properties is crucial for end-point control in BOF steelmaking. Traditional prediction models typically use multi-variable input and single-variable output approaches, neglecting the coupling relationships between different property indicators, making it difficult to predict multiple outputs simultaneously. Consequently, a multi-output prediction model based on the fusion of deep convolution and attention mechanism networks (FDCAN) is proposed. The model inputs include scalar data, such as the properties of raw materials and target molten steel, and time series data, such as lance height, oxygen supply intensity, and bottom air supply intensity during the blowing process. The FDCAN model utilizes a fully connected module to extract nonlinear features from scalar data and a deep convolution module to process time series data, capturing high-dimensional feature representations. The attention mechanism then assigns greater weight to significant features. Finally, multiple multi-layer perceptron modules predict the outputs—final carbon content, temperature, and phosphorus content. This structure allows FDCAN to learn complex relationships within the input data and between input and output variables. The effectiveness of the FDCAN model is validated using actual BOF steelmaking data, achieving hit rates of 95.14% for final carbon content within ±0.015 wt%, 84.72% for final temperature within ±15 °C, and 88.89% for final phosphorus content within ±0.005 wt%. Full article
(This article belongs to the Special Issue Process and Numerical Simulation of Oxygen Steelmaking)
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18 pages, 8361 KiB  
Article
Optimization of Oxygen Injection Conditions with Different Molten Steel Levels in the EAF Refining Process by CFD Simulation
by Perawat Thongjitr, Pruet Kowitwarangkul, Yotsakorn Pratumwal and Somboon Otarawanna
Metals 2023, 13(9), 1507; https://doi.org/10.3390/met13091507 - 22 Aug 2023
Cited by 3 | Viewed by 2105
Abstract
In electric arc furnace (EAF) steelmaking, oxygen jets play a crucial role in controlling stirring ability, chemical reactions, and energy consumption. During the EAF lifetime, refractory wear leads to a decrease in the molten steel level and an increase in the nozzle-to-steel distance, [...] Read more.
In electric arc furnace (EAF) steelmaking, oxygen jets play a crucial role in controlling stirring ability, chemical reactions, and energy consumption. During the EAF lifetime, refractory wear leads to a decrease in the molten steel level and an increase in the nozzle-to-steel distance, thereby negatively affecting the overall energy efficiency of the process. The objective of this study is to optimize the energy efficiency of the EAF refining process by adjusting the nozzle flow conditions and conducting an analysis of jet performance using computational fluid dynamics (CFD) simulation. Three types of injection jets were considered: the conventional jet, the CH4 coherent jet, and the CH4 + O2 coherent jet. The findings reveal that the shrouded flame of the coherent jet enhances jet performance by maintaining the maximum velocity, extending the potential core length, and increasing the penetration depth in the molten steel bath. To maintain the jet performance in response to an increased nozzle-to-steel distance resulting from refractory wear, transitions from the conventional jet to the CH4 coherent jet and the CH4 + O2 coherent jet are recommended once the nozzle-to-steel distance increases from its initial level of 1000 mm to 1500 mm and 2000 mm, respectively. Full article
(This article belongs to the Special Issue Process and Numerical Simulation of Oxygen Steelmaking)
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15 pages, 3853 KiB  
Article
Determination of Interaction Parameters between Mn and Al and the Influence of Mn on Al2O3 Inclusions Formation in High Mn and Al Content Fe-Mn-Al-O Melts at 1873 K
by Jie Zhang, Xinru Luo, Baijun Yan, Daya Wang and Hongbo Liu
Metals 2023, 13(8), 1500; https://doi.org/10.3390/met13081500 - 21 Aug 2023
Viewed by 1236
Abstract
For the purpose of determining the interaction parameters between Mn and Al, and the influence of Mn on Al2O3 inclusions formation in the Fe-Mn-Al-O melts with high Mn and Al contents, three groups of Fe-Mn-Al-O melts with the initial Al [...] Read more.
For the purpose of determining the interaction parameters between Mn and Al, and the influence of Mn on Al2O3 inclusions formation in the Fe-Mn-Al-O melts with high Mn and Al contents, three groups of Fe-Mn-Al-O melts with the initial Al content of 3, 5, and 7 mass% and different Mn contents were equilibrated with pure solid Al2O3 in an Al2O3 crucible at 1873 K and Ar-H2 atmosphere. Then, the interaction parameters between Mn and Al were deduced using the WIPF (Wagner’s Interaction Parameter Formalism) and the R-K polynomial (Redlich-Kister type polynomial), respectively. From the WIPF, the first- and second-order interaction parameters, eAlMn and rAlMn, were determined to be 0.0292 and −0.00016, respectively. From the R-K polynomial, the binary interaction parameters, ΩMn-Al0 and ΩMn-Al1, were determined to be 73,439 J/mol and −34,919 J/mol, respectively. The applicability of the WIPF to high Mn and Al content Fe-Mn-Al-O melts was investigated by comparing the Al activity calculated by the WIPF and the R-K polynomial using the obtained data. The results showed that WIPF can be used in high Mn and Al content melts in the current concentration range. Further from the iso-activity contours of Al, the activity of Al increases with increasing Al or Mn content. Finally, the thermodynamic calculations show that the addition of Mn decreases the equilibrium O content at the same Al content, making the formation of Al2O3 inclusions easier. Full article
(This article belongs to the Special Issue Process and Numerical Simulation of Oxygen Steelmaking)
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20 pages, 6093 KiB  
Article
Numerical Simulation of the Slag Splashing Process in A 120 Ton Top-Blown Converter
by Guang Yang, Baokuan Li, Meijia Sun, Deyue Qin and Liangcai Zhong
Metals 2023, 13(5), 940; https://doi.org/10.3390/met13050940 - 12 May 2023
Cited by 3 | Viewed by 1642
Abstract
Slag splashing operations at the end of the converter blow process can improve the furnace liner life and the converter operation rate. However, the effect of factors on slag splashing at actual dimensions is yet to be fully understood. A three-dimensional transient mathematical [...] Read more.
Slag splashing operations at the end of the converter blow process can improve the furnace liner life and the converter operation rate. However, the effect of factors on slag splashing at actual dimensions is yet to be fully understood. A three-dimensional transient mathematical model coupled with the response surface analysis has been established to investigate the effects of the amount of remaining slag, oxygen lance height, and top-blowing nitrogen flowrate on the slag splashing process in a 120 ton top-blown converter. The predicted splashing density is validated by the experimental data. The numerical simulation results show that the splashing density and the splashing area ratio increase with the amount of remaining slag, which has the greatest effect on slag splashing. As the oxygen lance height decreases, the splashing density and the splashing area ratio first increase and then decrease. The top-blowing nitrogen flowrate is positively correlated with the splashing area ratio. When the oxygen lance height is high, the impact of the top-blowing nitrogen flowrate on the splashing density is not significant. The splashing density increases with increasing top-blowing nitrogen flowrate as the oxygen lance height is low. Full article
(This article belongs to the Special Issue Process and Numerical Simulation of Oxygen Steelmaking)
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17 pages, 4787 KiB  
Article
Application of MLR, BP and PCA-BP Neural Network for Predicting FeO in Bottom-Blowing O2-CaO Converter
by Xin Ren, Kai Dong, Chao Feng, Rong Zhu, Guangsheng Wei and Chunyang Wang
Metals 2023, 13(4), 782; https://doi.org/10.3390/met13040782 - 16 Apr 2023
Cited by 3 | Viewed by 1525
Abstract
In order to accurately predict the FeO content of slag in the bottom-blowing O2-CaO process of the dephosphorization converter, multiple linear regression model, backpropagation (BP) neural network model and principal component analysis–backpropagation (PCA-BP) combined with neural network model were established to [...] Read more.
In order to accurately predict the FeO content of slag in the bottom-blowing O2-CaO process of the dephosphorization converter, multiple linear regression model, backpropagation (BP) neural network model and principal component analysis–backpropagation (PCA-BP) combined with neural network model were established to predict the FeO content of slag. It was found that the PCA-BP combined neural network model has the highest prediction accuracy by using principal component analysis to reduce the dimension of influencing factors of FeO content in slag and eliminate the correlation between input variables. The average absolute error is 1.178%, which is 0.78% lower than that of multiple linear regression model and 0.453% lower than that of multiple linear regression model. When the prediction error range is 3.0%, the prediction hit rate of the model is 96%, and when the prediction error range is 2.0%, the prediction hit rate of the model is 78%. The prediction model has important reference value for actual production. Full article
(This article belongs to the Special Issue Process and Numerical Simulation of Oxygen Steelmaking)
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15 pages, 9453 KiB  
Article
Numerical Simulation of Motion and Distribution of Powder Particles Injected from a Nozzles-Twisted Oxygen Lance in BOF Steelmaking
by Lin Li, Shan Yu, Ye Sun, Yan Liu, Ren Chen and Peiwen Hu
Metals 2023, 13(2), 211; https://doi.org/10.3390/met13020211 - 21 Jan 2023
Cited by 2 | Viewed by 1683
Abstract
The pulverized lime/limestone injection by top oxygen blowing lance during the basic oxygen furnace (BOF) process has gained much interest in recent years due to its advantages in helping slag formation and consequently in promoting refining reactions such as dephosphorization. In this pneumatic [...] Read more.
The pulverized lime/limestone injection by top oxygen blowing lance during the basic oxygen furnace (BOF) process has gained much interest in recent years due to its advantages in helping slag formation and consequently in promoting refining reactions such as dephosphorization. In this pneumatic process, understanding the motion behavior and distribution of the powder particles in the furnace is of importance for regulating and designing this refining system reliably and efficiently. In this study, limestone powder top blowing through a novel nozzles-twisted oxygen lance during a BOF process is proposed and the process is simulated by establishing a multi-fluid flow model. The coupled fluid flow of gaseous oxygen and liquid steel is predicted by the volume of fluid (VOF) method, and the motion of the limestone particles is tracked by the discrete phase model (DPM). The results show that the powder injection has little effect on cavity depth of the oxygen-powder mixture jets of the nozzles-twisted lance, but decreases cavity width. During the blowing process, most of the powder particles gather around hot spots while the rest are taken out of the furnace by the reflecting oxygen stream or penetrate into the molten bath. The generated swirling flow of the nozzles-twisted oxygen lance enables a decrease in the amount of the powder particles carried by the reflecting stream and going into the molten bath, through changing the motion paths of the powder particles. As a result, the concentration distribution of the powder particles in the molten bath varies. It could be suggested that for the limestone powder injection the preferred nozzle twist angle of the oxygen lance is 10° due to the favorable conditions for dephosphorization. Full article
(This article belongs to the Special Issue Process and Numerical Simulation of Oxygen Steelmaking)
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13 pages, 4660 KiB  
Article
The Growth of Intermetallic Compounds and Its Effect on Bonding Properties of Cu/Al Clad Plates by CFR
by Long Li, Guangping Deng, Weiguo Zhai, Sha Li, Xiangyu Gao and Tao Wang
Metals 2022, 12(11), 1995; https://doi.org/10.3390/met12111995 - 21 Nov 2022
Cited by 4 | Viewed by 1606
Abstract
Cu/Al clad plates prepared using a corrugated + flat rolling (CFR) technique were annealed at 300–450 °C for 10–240 min. Furthermore, the interfacial diffusion behavior and the bonding properties of the Cu/Al clad plates were studied in detail. The results demonstrated that, at [...] Read more.
Cu/Al clad plates prepared using a corrugated + flat rolling (CFR) technique were annealed at 300–450 °C for 10–240 min. Furthermore, the interfacial diffusion behavior and the bonding properties of the Cu/Al clad plates were studied in detail. The results demonstrated that, at the initial stage of the annealing process, the development of the first IMCs layer was restrained by the high atomic concentration gradient in the new bonding interface zone, and the second intermetallic compounds (IMCs) layer preferentially formed in the new bonding interface zone, leading to a slight increase in the growth activation energy of the clad plates. In addition, the atoms’ diffusion behavior at the peak and trough interfaces was not significantly affected by the matrix microstructure, and there was no discernible difference in the growth activation energy at these two positions. Ultimately, it was shown that the maximum average peel strength at the peak and trough interfaces reached 53.07 N/mm and 41.23 N/mm, respectively, when annealing at 350 °C for 10 min. Full article
(This article belongs to the Special Issue Process and Numerical Simulation of Oxygen Steelmaking)
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