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Metals
Special Issues
Green and Intelligent Steelmaking Technologies with Low Carbon Emissions
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Green and Intelligent Steelmaking Technologies with Low Carbon Emissions

A special issue of Metals (ISSN 2075-4701).

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 4158

Special Issue Editors

Dr. Ming Lv

E-Mail Website
Guest Editor
College of Metallurgical Engineering, Xi'an University of Architecture and Technology, Xi’an 710055, China
Interests: steelmaking; non-metallic inclusions; clean steel; secondary refining; slag system
Special Issues, Collections and Topics in MDPI journals
Dr. Lingzhi Yang

E-Mail Website
Guest Editor
School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China
Interests: simulation; theoretical model; steelmaking; comprehensive utilization of complex ores
Special Issues, Collections and Topics in MDPI journals
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

Special Issue Information

Dear Colleagues,

Reducing carbon emissions from the steel industry is vital for achieving the strategic goals of carbon peak and carbon neutrality. Steelmaking is an important link in the metallurgical process of iron and steel, and it is necessary to carry out relative technical innovation in order to reduce carbon emissions from steelmaking processes. Currently, converter steelmaking and electric arc furnace steelmaking are the main steelmaking methods and in recent years, many green and intelligent steelmaking technologies have been proposed and developed, especially in slag utilization, process optimization, green electric steelmaking, intelligent smelting and so on.

Dr. Ming Lv
Dr. Lingzhi Yang
Dr. Guangsheng Wei
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

  • electric arc furnace steelmaking
  • converter steelmaking
  • renewable energy utilization in steelmaking processes
  • carbon capture, utilization and storage (CCUS) in steelmaking processes
  • resource utilization of dust and slag
  • intelligent steelmaking technologies
  • steelmaking process control model

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Published Papers (3 papers)

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Research

14 pages, 3496 KiB  
Article
Analysis of Longitudinal Cracking and Mold Flux Optimization in High-Speed Continuous Casting of Hyper-Peritectic Steel Thin Slabs
by Zhipeng Yuan, Liguang Zhu, Xingjuan Wang and Kaixuan Zhang
Metals 2024, 14(8), 909; https://doi.org/10.3390/met14080909 - 11 Aug 2024
Viewed by 1144
Abstract
Longitudinal crack defects are a frequent occurrence on the surface of thin slabs during the high-speed continuous casting process. Therefore, this study undertakes a detailed analysis of the solidification characteristics of hyper-peritectic steel thin slabs. By establishing a three-dimensional heat transfer numerical model [...] Read more.
Longitudinal crack defects are a frequent occurrence on the surface of thin slabs during the high-speed continuous casting process. Therefore, this study undertakes a detailed analysis of the solidification characteristics of hyper-peritectic steel thin slabs. By establishing a three-dimensional heat transfer numerical model of the thin slab, the formation mechanism of longitudinal cracks caused by uneven growth of the initial shell is determined. Based on the mechanism of longitudinal crack formation, by adjusting the performance parameters of the mold flux, the contradiction between the heat transfer control and lubrication improvement of the mold flux is fully coordinated, further reducing the incidence of longitudinal cracks on the surface of the casting thin slab. The results show that, using the optimized mold flux, the basicity increases from 1.60 to 1.68, the F- mass fraction increases from 10.67% to 11.22%, the Na2O mass fraction increases from 4.35% to 5.28%, the Li2O mass fraction increases from 0.68% to 0.75%, and the carbon mass fraction reduces from 10.86% to 10.47%. The crystallization performance and rheological properties of the mold flux significantly improve, reducing the heat transfer performance while ensuring the lubrication ability of the molten slag. After optimizing the mold flux, a surface detection system was used to statistically analyze the longitudinal cracks on the surface of the casting thin slab. The proportion of longitudinal cracks (crack length/steel coil length, where each coil produced is about 32 m long) on the surface of the thin slab decreases from 0.056% to 0.031%, and the surface quality of the thin slab significantly improves. Full article
(This article belongs to the Special Issue Green and Intelligent Steelmaking Technologies with Low Carbon Emissions)
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14 pages, 7327 KiB  
Article
The Modification of Aluminum Oxide Inclusions in Bearing Steel under Different Cleanliness Conditions by Rare Earth Elements
by Weining Wang, Wenzhi Xia, Yun Zhou, Aijun Deng, Guangda Bao, Zhiyou Liao and Haichuan Wang
Metals 2024, 14(8), 861; https://doi.org/10.3390/met14080861 - 26 Jul 2024
Viewed by 1058
Abstract
The impact of rare earth treatment on the chemical morphology evolution of non-metallic inclusions in bearing steel under different initial cleanliness conditions was studied through simulation. Thermodynamic calculations indicate that at an oxygen content of 0.001%, the evolution route of inclusions with increasing [...] Read more.
The impact of rare earth treatment on the chemical morphology evolution of non-metallic inclusions in bearing steel under different initial cleanliness conditions was studied through simulation. Thermodynamic calculations indicate that at an oxygen content of 0.001%, the evolution route of inclusions with increasing Ce content was Al2O3 → CeAl11O18 + CeAlO3 → CeAlO3 + Ce2O2S → Ce2O2S → Ce2O2S + CeS. As the initial oxygen content decreases, the proportion of CeAlO3 decreases, leading to easier conversion of CeAlO3 to Ce2O2S. Vacuum induction furnace experiments demonstrated that with an oxygen content of 0.001%, an increase in Ce content results in a gradual rise in the proportion of inclusions in steel sized 1~2 μm. In contrast, the proportion of inclusions sized 2~5 μm decreases. Consequently, the overall content of inclusions in steel decreases, along with a reduction in both the number density and average size of inclusions. Introducing bearing steel melt with approximately 0.01% Ce rare earth to bearing steel with initial oxygen contents of 0.0005%, 0.001%, and 0.0015% showed an evolution of inclusions from Ce2O2S and CeS to Ce2O3, CeAlO3, etc. The average inclusion size significantly increased from 0.7 μm to 2.16 μm. Morphologically, the transition of inclusions from precipitated to polymerized forms occurred as the initial oxygen content rose. High-temperature laser confocal microscopy experiments demonstrated that inclusions in low cleanliness conditions tend to agglomerate more than those in high cleanliness conditions, contributing to the increase in average size. Full article
(This article belongs to the Special Issue Green and Intelligent Steelmaking Technologies with Low Carbon Emissions)
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25 pages, 19567 KiB  
Article
Evaluation of Energy Utilization Efficiency and Optimal Energy Matching Model of EAF Steelmaking Based on Association Rule Mining
by Lingzhi Yang, Zhihui Li, Hang Hu, Yuchi Zou, Zeng Feng, Weizhen Chen, Feng Chen, Shuai Wang and Yufeng Guo
Metals 2024, 14(4), 458; https://doi.org/10.3390/met14040458 - 12 Apr 2024
Cited by 2 | Viewed by 1269
Abstract
In the iron and steel industry, evaluating the energy utilization efficiency (EUE) and determining the optimal energy matching mode play an important role in addressing increasing energy depletion and environmental problems. Electric Arc Furnace (EAF) steelmaking is a typical short crude steel production [...] Read more.
In the iron and steel industry, evaluating the energy utilization efficiency (EUE) and determining the optimal energy matching mode play an important role in addressing increasing energy depletion and environmental problems. Electric Arc Furnace (EAF) steelmaking is a typical short crude steel production route, which is characterized by an energy-intensive fast smelting rhythm and diversified raw charge structure. In this paper, the energy model of the EAF steelmaking process is established to conduct an energy analysis and EUE evaluation. An association rule mining (ARM) strategy for guiding the EAF production process based on data cleaning, feature selection, and an association rule (AR) algorithm was proposed, and the effectiveness of this strategy was verified. The unsupervised algorithm Auto-Encoder (AE) was adopted to detect and eliminate abnormal data, complete data cleaning, and ensure data quality and accuracy. The AE model performs best when the number of nodes in the hidden layer is 18. The feature selection determines 10 factors such as the hot metal (HM) ratio and HM temperature as important data features to simplify the model structure. According to different ratios and temperatures of the HM, combined with k-means clustering and an AR algorithm, the optimal operation process for the EUE in the EAF steelmaking under different smelting modes is proposed. The results indicated that under the conditions of a low HM ratio and low HM temperature, the EUE is best when the power consumption in the second stage ranges between 4853 kWh and 7520 kWh, the oxygen consumption in the second stage ranges between 1816 m3 and 1961 m3, and the natural gas consumption ranges between 156 m3 and 196 m3. Conversely, under the conditions of a high HM ratio and high HM temperature, the EUE tends to decrease, and the EUE is best when the furnace wall oxygen consumption ranges between 4732 m3 and 5670 m3, and the oxygen consumption in the second stage ranges between 1561 m3 and 1871 m3. By comparison, under different smelting modes, the smelting scheme obtained by the ARM has an obvious effect on the improvement of the EUE. With a high EUE, the improvement of the A2B1 smelting mode is the most obvious, from 24.7% to 53%. This study is expected to provide technical ideas for energy conservation and emission reduction in the EAF steelmaking process in the future. Full article
(This article belongs to the Special Issue Green and Intelligent Steelmaking Technologies with Low Carbon Emissions)
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