Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.4
2.9
Journals
Metals
Special Issues
Modeling and Simulation of Metallurgical Process
share announcement

Modeling and Simulation of Metallurgical Process

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 (31 March 2024) | Viewed by 3040

Special Issue Editors

Prof. Dr. Hongliang Zhang

E-Mail Website
Guest Editor
National Engineering Research Center of Low-Carbon Nonferrous Metallurgy, Central South University, Changsha, 410083, China
Interests: intelligent metallurgy; numerical modelling; simulation and optimization; metallurgical process engineering; metallurgical smart factory
Prof. Dr. Hesong Li

E-Mail Website
Guest Editor
School of Energy science and Engineering, Central South University, Changsha 410083, China
Interests: energy science and engineering; engineering thermal physics; simulation and modelling; industrial digitization and virtual technology

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 recent research related to modeling and simulation of metallurgical process to face current challenges in metal production.

In this Special Issue, both fundamental insights and practical foresights are greatly welcome in the form of research article or review. Research areas may include (but are not limited to) the following: thermodynamics, kinetics, physical modelling, numerical simulation, computational fluid dynamics, molecular simulation, 3D visualization, artificial intelligence, big data, and cloud computation. We look forward to receiving your contributions.

Prof. Dr. Hongliang Zhang
Prof. Dr. Hesong Li
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

  • thermodynamics and kinetics
  • intelligent metallurgy
  • non-ferrous metallurgy
  • ironmaking and steelmaking
  • control of metallurgical processes
  • simulation of metallurgical processes
  • artificial intelligence, big data and cloud computation

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 5274 KiB  
Article
Long Short-Term Memory Parameter Optimization Based on Improved Sparrow Search Algorithm for Molten Iron Quality Prediction
by Ziwen Zhang, Ruiyao Zhang and Ping Zhou
Metals 2024, 14(5), 529; https://doi.org/10.3390/met14050529 - 30 Apr 2024
Viewed by 383
Abstract
Blast furnace (BF) ironmaking is a key process in iron and steel production. Because BF ironmaking is a dynamic time series process, it is more appropriate to use a recurrent neural network for modeling. The long short-term memory (LSTM) network is commonly used [...] Read more.
Blast furnace (BF) ironmaking is a key process in iron and steel production. Because BF ironmaking is a dynamic time series process, it is more appropriate to use a recurrent neural network for modeling. The long short-term memory (LSTM) network is commonly used to model time series data. However, its model performance and generalization ability heavily depend on the parameter configuration. Therefore, it is necessary to study parameter optimization for the LSTM model. The sparrow search algorithm (SSA) holds advantages over traditional optimization algorithms in several aspects, such as no need for prior knowledge, fewer parameters, fast convergence, and high scalability. However, the algorithm still faces some challenges, such as the tendency to become trapped in the local optimum and the imbalance between global search ability and local search ability. Therefore, on the basis of SSA, this study examined the Levy flight strategy, sine search strategy, and step size factor adjustment strategy to improve it. This algorithm, improved by three strategies, is called the improved sparrow search algorithm (ISSA). Then, the ISSA-LSTM model was established. Furthermore, considering the limitations of SSA in dealing with multi-objective problems, the fast non-dominated sorting genetic algorithm (NSGAII) was introduced, and the ISSA-NSGAII model was established. Finally, experimental validation was performed using real blast furnace operation data, which demonstrated the proposed algorithm’s superiority in parameter optimization for the LSTM model and prediction for real industrial data. Full article
(This article belongs to the Special Issue Modeling and Simulation of Metallurgical Process)
►▼ Show Figures

Figure 1

19 pages, 3631 KiB  
Article
Analysis of the Varying Thickness Rolling Process Based on the Principle of Pre-Displacement
by Shiyu Yang, Hongmin Liu, Dongcheng Wang, Guodong Wang and Xiaozheng Cao
Metals 2023, 13(11), 1799; https://doi.org/10.3390/met13111799 - 25 Oct 2023
Viewed by 988
Abstract
This paper conducts a comprehensive analysis of the varying thickness rolling process, grounded in the principle of pre-displacement. Within the rolling deformation zone, the subject is divided into three distinct areas: the backward slip zone, sticking zone, and forward slip zone. We propose [...] Read more.
This paper conducts a comprehensive analysis of the varying thickness rolling process, grounded in the principle of pre-displacement. Within the rolling deformation zone, the subject is divided into three distinct areas: the backward slip zone, sticking zone, and forward slip zone. We propose a model that delineates the sticking-sliding motion between the rolled piece and the roller, all within the context of the rolling deformation zone, and derive a calculation method for determining the longitudinal lengths of both the sliding and sticking zones within this area. Unlike existing full sliding and full sticking models, our sticking-sliding model offers a novel perspective, overcoming inherent limitations when analyzing the rolling process. Furthermore, we establish models to describe the neutral angle and forward slip value and to predict the longitudinal length of the rolled piece during the varying thickness rolling process. A comparison of our calculated results with experimental data demonstrates the feasibility and effectiveness of the method, thereby laying a robust theoretical foundation for the varying thickness rolling process. Full article
(This article belongs to the Special Issue Modeling and Simulation of Metallurgical Process)
►▼ Show Figures

Figure 1

13 pages, 4849 KiB  
Article
Numerical Investigation of Electro-Thermal Field Distribution Law of Busbar under Different Operating Conditions
by Wenyuan Hou, Kaibing Sun, Shuaigeng Sun and Mao Li
Metals 2023, 13(8), 1361; https://doi.org/10.3390/met13081361 - 28 Jul 2023
Cited by 2 | Viewed by 1139
Abstract
The electro-thermal state of a busbar system of electrolysis cells for aluminum production represents the main factor affecting hydromagnetic stability and current distribution. Based on the busbar system of a 500 kA aluminum electrolytic cell, an overall busbar electro-thermal field coupling calculation model [...] Read more.
The electro-thermal state of a busbar system of electrolysis cells for aluminum production represents the main factor affecting hydromagnetic stability and current distribution. Based on the busbar system of a 500 kA aluminum electrolytic cell, an overall busbar electro-thermal field coupling calculation model was established based on ANSYS. The characteristics of busbar temperature, current density, and voltage drop distribution were analyzed. In addition, the electro-thermal distribution of the busbar system was simulated under different current intensities, ambient temperatures, and heat transfer coefficients. The results show that the temperature distribution of the riser busbar and the cathode busbar is higher in the middle location and tends to decrease along the two sides. Differences in heat conduction and heat dissipation environment are the main factors affecting the distribution of the busbar system’s electro-thermal field, while the Joule heat of the current is not the major factor. Increasing the current intensity will increase the average temperature and average voltage drop of the busbar. With an increase in the ambient temperature, the average busbar temperature increases significantly, and the voltage drop of the busbar also increases. With an increase in heat transfer coefficient, the average temperature and voltage drop of the busbar decreases. Full article
(This article belongs to the Special Issue Modeling and Simulation of Metallurgical Process)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop