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5.1
2.8
Journals
Processes
Special Issues
Advanced Ladle Metallurgy and Secondary Refining
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Advanced Ladle Metallurgy and Secondary Refining

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 10 December 2024 | Viewed by 4409

Special Issue Editors

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
Prof. Dr. Alberto N. Conejo

E-Mail Website
Guest Editor
School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: slag/metal reactions; fluid flow phenomena in ladles; EAF steelmaking; gas/solid reactions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ladle used to primarily be a vessel that transferred liquid steel to other reactors. In the 1960s, bottom bubbling, vacuum degassing, powder injection and electromagnetic stirring techniques were also applied to the ladle. A great number of new secondary refining technologies have been developed, such as, for example, LF, RH, DH, VD, VOD, VAD, SL, CAS-OB, ASEA-SKF, SSRF, REDA, etc. The research on ladle metallurgy can be dated back to early the 1970s and was carried out by Professor Julian Szekely and coworkers. After 60 years of development, the secondary refining of liquid steel has developed with the evolution and demand for high-quality and clean steel production. Extensive physical models, mathematical models and industrial pilot studies on ladle metallurgy have been carried out.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) ladle metallurgy, secondary refining, clean steel technologies, slags, refractories, nonmetallic inclusions, and metallurgical equipment development. We look forward to receiving your contributions.

Dr. Chao Chen
Prof. Dr. Alberto N. Conejo
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. Processes 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 2400 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

  • ladle
  • secondary refining
  • clean steel
  • inclusions
  • steelmaking
  • refractory
  • physical modeling
  • CFD
  • slags
  • vacuum degassing

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Published Papers (3 papers)

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Research

19 pages, 22292 KiB  
Article
An Efficient and Accurate Quality Inspection Model for Steel Scraps Based on Dense Small-Target Detection
by Pengcheng Xiao, Chao Wang, Liguang Zhu, Wenguang Xu, Yuxin Jin and Rong Zhu
Processes 2024, 12(8), 1700; https://doi.org/10.3390/pr12081700 - 14 Aug 2024
Viewed by 690
Abstract
Scrap steel serves as the primary alternative raw material to iron ore, exerting a significant impact on production costs for steel enterprises. With the annual growth in scrap resources, concerns regarding traditional manual inspection methods, including issues of fairness and safety, gain increasing [...] Read more.
Scrap steel serves as the primary alternative raw material to iron ore, exerting a significant impact on production costs for steel enterprises. With the annual growth in scrap resources, concerns regarding traditional manual inspection methods, including issues of fairness and safety, gain increasing prominence. Enhancing scrap inspection processes through digital technology is imperative. In response to these concerns, we developed CNIL-Net, a scrap-quality inspection network model based on object detection, and trained and validated it using images obtained during the scrap inspection process. Initially, we deployed a multi-camera integrated system at a steel plant for acquiring scrap images of diverse types, which were subsequently annotated and employed for constructing an enhanced scrap dataset. Then, we enhanced the YOLOv5 model to improve the detection of small-target scraps in inspection scenarios. This was achieved by adding a small-object detection layer (P2) and streamlining the model through the removal of detection layer P5, resulting in the development of a novel three-layer detection network structure termed the Improved Layer (IL) model. A Coordinate Attention mechanism was incorporated into the network to dynamically learn feature weights from various positions, thereby improving the discernment of scrap features. Substituting the traditional non-maximum suppression algorithm (NMS) with Soft-NMS enhanced detection accuracy in dense and overlapping scrap scenarios, thereby mitigating instances of missed detections. Finally, the model underwent training and validation utilizing the augmented dataset of scraps. Throughout this phase, assessments encompassed metrics like mAP, number of network layers, parameters, and inference duration. Experimental findings illustrate that the developed CNIL-Net scrap-quality inspection network model boosted the average precision across all categories from 88.8% to 96.5%. Compared to manual inspection, it demonstrates notable advantages in accuracy and detection speed, rendering it well suited for real-world deployment and addressing issues in scrap inspection like real-time processing and fairness. Full article
(This article belongs to the Special Issue Advanced Ladle Metallurgy and Secondary Refining)
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16 pages, 4112 KiB  
Article
Method for Dynamic Prediction of Oxygen Demand in Steelmaking Process Based on BOF Technology
by Kaitian Zhang, Zhong Zheng, Liu Zhang, Yu Liu and Sujun Chen
Processes 2023, 11(8), 2404; https://doi.org/10.3390/pr11082404 - 10 Aug 2023
Cited by 5 | Viewed by 1657
Abstract
Oxygen is an important energy medium in the steelmaking process. The accurate dynamic prediction of oxygen demand is needed to guarantee molten steel quality, improve the production rhythm, and promote the collaborative optimization of production and energy. In this work, a analysis of [...] Read more.
Oxygen is an important energy medium in the steelmaking process. The accurate dynamic prediction of oxygen demand is needed to guarantee molten steel quality, improve the production rhythm, and promote the collaborative optimization of production and energy. In this work, a analysis of the mechanism and of industrial big data was undertaken, and we found that the characteristic factors of Basic Oxygen Furnace (BOF) oxygen consumption were different in different modes, such as duplex dephosphorization, duplex decarbonization, and the traditional mode. Based on this, a dynamic-prediction modeling method for BOF oxygen demand considering mode classification is proposed. According to the characteristics of BOF production organization, a control module based on dynamic adaptions of the production plan was researched to realize the recalculation of the model predictions. A simulation test on industrial data revealed that the average relative error of the model in each BOF mode was less than 5% and the mean absolute error was about 450 m3. Moreover, an accurate 30-minute-in-advance prediction of dynamic oxygen demand was realized. This paper provides the method support and basis for the long-term demand planning of the static balance and the short-term real-time scheduling of the dynamic balance of oxygen. Full article
(This article belongs to the Special Issue Advanced Ladle Metallurgy and Secondary Refining)
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14 pages, 7709 KiB  
Article
Study on the Evolution Law of Inclusions in the Whole Process and Evaluation of Cleanliness in Start and End of Casting Billets of 42CrMo-S Steel
by Lidong Xing, Bo Wang, Yanping Bao and Min Wang
Processes 2023, 11(7), 2184; https://doi.org/10.3390/pr11072184 - 21 Jul 2023
Cited by 1 | Viewed by 1246
Abstract
To investigate the evolution law of inclusions in 42CrMo-S steel, this paper samples and analyzes the steel during its refining process as well as the head and tail billets. An oxygen and nitrogen analyzer, a scanning electron microscope (SEM) equipped with energy-dispersive X-ray [...] Read more.
To investigate the evolution law of inclusions in 42CrMo-S steel, this paper samples and analyzes the steel during its refining process as well as the head and tail billets. An oxygen and nitrogen analyzer, a scanning electron microscope (SEM) equipped with energy-dispersive X-ray spectrometry (EDS), and an ASPEX automatic inclusion scanning electron microscope are employed to analyze the cleanliness level of the molten steel in the refining stage and the head and tail billets. The results demonstrate that the total oxygen content at the end of LF slagging is 10.2 ppm, indicating that the refining slag has an excellent deoxygenation effect. During the RH refining process, the total oxygen content of the molten steel diminishes to less than 10 ppm and reaches 6.3 ppm at end-RH. The nitrogen content in the molten steel gradually increases during the smelting process and attains 65 ppm at end-RH. Upon arrival at LF, pure Al2O3 plays the role of the primary inclusions in the molten steel. Afterwards, the pure Al2O3 inclusions transform into Mg-Al spinel-type inclusions, Al2O3-MgO-CaO inclusions, and Al2O3-CaO inclusions. The number of CaS-type inclusions in the steel reaches the maximum after feeding the S wire. In the RH refining stage, the percentage of inclusions with a size less than 5 μm is maintained above 90%. Finally, the cleanliness level of the head and tail billets (the start and end of a casting sequence) is analyzed, and it is recommended that the cut scrap length for the head billet is 0.3 m and the reasonable cutting scrap length for the tail billet is 1 m. Full article
(This article belongs to the Special Issue Advanced Ladle Metallurgy and Secondary Refining)
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