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Metallurgical Process Simulation and Optimization (3rd Edition)
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Metallurgical Process Simulation and Optimization (3rd Edition)

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 1323

Special Issue Editors

Prof. Dr. Qing Liu

E-Mail Website
Guest Editor
State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China
Interests: steelmaking; numerical modelling; simulation and optimization; metallurgical process engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Jiangshan Zhang

E-Mail Website
Guest Editor
State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China
Interests: steelmaking; casting; simulation and modelling; intelligent metallurgy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Metallurgy involves the art and science of extracting metals from their ores and modifying the metals for use. With thousands of years of development, many interdisciplinary technologies have been introduced into this traditional and large-scale industry. In modern metallurgical practices, modelling and simulation have been widely used to provide solutions for design, control, optimization, and visualization, and tend to be increasingly significant in the progress of digital transformation and intelligent metallurgy.

This Special Issue aims to provide an opportunity for researchers from both academia and industry to share their advances pertinent to the Special Issue “Metallurgical Process Simulation and Optimization (3rd Edition)” which covers the process of electric/oxygen steelmaking, secondary metallurgy, (continuous) casting, and processing. Both fundamental insights and practical foresights are greatly welcome in the form of research article or review, such as thermodynamics, kinetics, physical modelling, numerical simulation, CFD, 3D visualization, microstructural evolution, quality control, artificial intelligence, big data, and cloud computation.

Prof. Dr. Qing Liu
Dr. Jiangshan Zhang
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. Materials is an international peer-reviewed open access semimonthly 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

  • thermodynamics and kinetics
  • primary steelmaking and secondary metallurgy
  • solidification and casting process
  • microstructure and property
  • metallurgical process engineering
  • artificial intelligence, big data and cloud computation

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Related Special Issue

Published Papers (2 papers)

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Research

26 pages, 8774 KiB  
Article
Analysis of Multi-Zone Reaction Mechanisms in BOF Steelmaking and Comprehensive Simulation
by Zicheng Xin, Qing Liu, Jiangshan Zhang and Wenhui Lin
Materials 2025, 18(5), 1038; https://doi.org/10.3390/ma18051038 - 26 Feb 2025
Viewed by 335
Abstract
The BOF steelmaking process involves complex physical and chemical reactions, making precise control challenging when relying solely on human experience. Therefore, understanding the reaction mechanisms and developing simulation models for the BOF process are crucial for enhancing control accuracy and advancing intelligent steelmaking. [...] Read more.
The BOF steelmaking process involves complex physical and chemical reactions, making precise control challenging when relying solely on human experience. Therefore, understanding the reaction mechanisms and developing simulation models for the BOF process are crucial for enhancing control accuracy and advancing intelligent steelmaking. In this study, the physical and chemical behaviors in various reaction zones were first analyzed under actual production conditions using the multi-zone reaction theory. Then, a comprehensive mechanism model for BOF steelmaking was established, and an integrated simulation of metallurgical reactions during the BOF steelmaking process was performed using FactSage thermodynamic software. Finally, the validity of this comprehensive model was verified through actual production data. The results show that the relative deviation of the cumulative decarburization rate ranges from −0.66% to 1.68%, while the absolute deviation of the calculated carbon content curve compared to the actual curve is less than 0.12%. This research helps clarify the variation patterns of key process parameters in BOF steelmaking, playing a significant role in advancing the intelligence of the BOF steelmaking process. Full article
(This article belongs to the Special Issue Metallurgical Process Simulation and Optimization (3rd Edition))
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23 pages, 6367 KiB  
Article
Prediction of Silicon Content in a Blast Furnace via Machine Learning: A Comprehensive Processing and Modeling Pipeline
by Omer Raza, Nicholas Walla, Tyamo Okosun, Kosta Leontaras, Jason Entwistle and Chenn Zhou
Materials 2025, 18(3), 632; https://doi.org/10.3390/ma18030632 - 30 Jan 2025
Viewed by 811
Abstract
Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely [...] Read more.
Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely on a limited set of manually selected furnace parameters. Additionally, different BFs present a diverse set of operating parameters and state variables that are known to directly influence the hot metal’s silicon content, such as fuel injection, blast temperature, and raw material charge composition, among other process variables that have their own impacts. The expansiveness of the parameter set adds complexity to parameter selection and processing. This highlights the need for a comprehensive methodology to integrate and select from all relevant parameters for accurate silicon content prediction. Providing accurate silicon content predictions would enable operators to adjust furnace conditions dynamically, improving safety and reducing economic risk. To address these issues, a two-stage approach is proposed. First, a generalized data processing scheme is proposed to accommodate diverse furnace parameters. Second, a robust modeling pipeline is used to establish a machine learning (ML) model capable of predicting hot metal silicon content with reasonable accuracy. The method employed herein predicted the average Si content of the upcoming furnace cast with an accuracy of 91% among 200 target predictions for a specific furnace provisioned by the XGBoost model. This prediction is achieved using only the past shift’s operating conditions, which should be available in real time. This performance provides a strong baseline for the modeling approach with potential for further improvement through provision of real-time features. Full article
(This article belongs to the Special Issue Metallurgical Process Simulation and Optimization (3rd Edition))
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