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Metals
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Digitalization and Advanced Software Support of the Steelmaking Industry
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Digitalization and Advanced Software Support of the Steelmaking Industry

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 5868

Special Issue Editors

Dr. Vito Logar

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia
Interests: electric arc furnaces; modelling; optimization; software support
Dr. Amirhossein Fathi

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shiraz University, Shiraz 71557-13876, Iran
Interests: electric arc furnaces; modelling; energy recovery; energy utilization

Special Issue Information

Dear Colleagues,

Digitalization, advanced software support and optimized manufacturing practices are becoming a must in modern efficient industry. The areas of ironmaking and steelmaking are no exception since the increased market and environmental demands are higher than ever. High product quality standards, lower resource use, and the goals of reaching CO2 neutrality in the not-so-far future are encouraging researchers all over the world to devote extensive efforts to finding improved manufacturing practices and more efficient processes.

In the field of steelmaking, data acquisition and control systems, which are considered a prerequisite for digitalization and development of different process-improvement methods, have been present for decades; however, for a long time, their purpose was merely to provide basic process control, monitoring, and data storage. However, in the sense of the Industry 4.0, the potential of these systems is much greater. Large quantities of acquired process data, development of the advanced software solutions, which partially or completely rely on this data, and their integration into existent or new industrial IT and OT systems, can lead to improved process efficiency in many of the steelmaking fields.

Advanced software support solutions can in this sense reduce the role of the operators and their experience, which are in the steelmaking industry not negligible, and often lead to suboptimal process operation. Reduced insight into many of these processes due to the lack of process measurements represents another challenge for reaching the optimality of the processes. In this sense, the integration of different digital solutions into industrial infrastructure can thus lead to substantial improvements in either product quality, process reliability, or energy efficiency.

This Special Issue addresses new approaches and solutions in the digitalization of the steelmaking industry, which aim to improve different steelmaking processes in terms of their control, efficiency, reliability, and environmental impact. Manuscripts dealing with the practical implementation of the proposed solutions, their practical validation, and the before/after evaluation of their efficiency for a given process are highly desirable.

Dr. Vito Logar
Dr. Amirhossein Fathi
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

  • Advanced Software Support
  • Digitalization
  • Digital Twins
  • Energy Efficiency
  • GHG Emission Reduction
  • Informatization
  • Modelling
  • Optimization
  • Process Monitoring

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Published Papers (4 papers)

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Research

23 pages, 2193 KiB  
Article
Arc Quality Index Based on Three-Phase Cassie–Mayr Electric Arc Model of Electric Arc Furnace
by Aljaž Blažič, Igor Škrjanc and Vito Logar
Metals 2024, 14(3), 338; https://doi.org/10.3390/met14030338 - 15 Mar 2024
Viewed by 1182
Abstract
In steel recycling, the optimization of Electric Arc Furnaces (EAFs) is of central importance in order to increase efficiency and reduce costs. This study focuses on the optimization of electric arcs, which make a significant contribution to the energy consumption of EAFs. A [...] Read more.
In steel recycling, the optimization of Electric Arc Furnaces (EAFs) is of central importance in order to increase efficiency and reduce costs. This study focuses on the optimization of electric arcs, which make a significant contribution to the energy consumption of EAFs. A three-phase equivalent circuit integrated with the Cassie–Mayr arc model is used to capture the nonlinear and dynamic characteristics of arcs, including arc breakage and ignition process. A particle swarm optimization technique is applied to real EAF data containing current and voltage measurements to estimate the parameters of the Cassie–Mayr model. Based on the Cassie–Mayr arc parameters, a novel Arc Quality Index (AQI) is introduced in the study, which can be used to evaluate arc quality based on deviations from optimal conditions. The AQI provides a qualitative assessment of arc quality, analogous to indices such as arc coverage and arc stability. The study concludes that the AQI serves as an effective operational tool for EAF operators to optimize production and increase the efficiency and sustainability of steel production. The results underline the importance of understanding electric arc dynamics for the development of EAF technology. Full article
(This article belongs to the Special Issue Digitalization and Advanced Software Support of the Steelmaking Industry)
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24 pages, 5287 KiB  
Article
Sequential Regularization Method for the Identification of Mold Heat Flux during Continuous Casting Using Inverse Problem Solutions Techniques
by Haihui Zhang, Jiawei Zou and Pengcheng Xiao
Metals 2023, 13(10), 1685; https://doi.org/10.3390/met13101685 - 1 Oct 2023
Cited by 1 | Viewed by 1241
Abstract
A two-dimensional transient inverse heat-conduction problem (2DIHCP) was established to determine the mold heat flux using observed temperatures. The sequential regularization method (SRM) was used with zeroth-, first-, and second-order spatial regularization to solve the 2DIHCP. The accuracy of the 2DIHCP was investigated [...] Read more.
A two-dimensional transient inverse heat-conduction problem (2DIHCP) was established to determine the mold heat flux using observed temperatures. The sequential regularization method (SRM) was used with zeroth-, first-, and second-order spatial regularization to solve the 2DIHCP. The accuracy of the 2DIHCP was investigated under two strict test conditions (Case 1: heat flux with time-spatial periodically varying, and Case 2: that with sharp variations). The effects of the number of future time steps, regularization parameters, order of regularization, discrete grids, and time step size on the accuracy of the 2DIHCP were analyzed. The results showed that the minimum relative error (epred) of the predicted Case 1 heat flux is 5.05%, 5.39%, and 5.88% for zeroth-, first-, and second-order spatial regularization, respectively. The corresponding values for the predicted Case 2 heat flux are 6.31%, 6.30%, and 6.36%. Notably, zeroth- and first-order spatial regularization had higher accuracy than second-order spatial regularization, while zeroth-order spatial regularization was comparable to first-order. Additionally, first-order spatial regularization was more accurate in reconstructing heat flux containing sharp spatial variations. The CPU time of the predicted Case 2 heat flux is 1.71, 1.71, and 1.70 s for zeroth-, first-, and second-order spatial regularization, respectively. The corresponding values for the predicted Case 1 heat flux are 6.18, 6.15, and 6.17 s. It is noteworthy that the choice of spatial regularization order does not significantly impact the required computing time. Lastly, the minimum epred of Case 2 heat flux with zeroth-order spatial regularization is 7.96%, 6.42%, and 7.87% for time step sizes of 1/fs, 1/2fs, and 1/5fs, respectively. The accuracy of the inverse analysis displays an initial improvement followed by degradation as the time step size decreases. A recommended time step size is 1/2fs, where fs denotes the temperature-sampling rate. Full article
(This article belongs to the Special Issue Digitalization and Advanced Software Support of the Steelmaking Industry)
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12 pages, 3748 KiB  
Article
Model-Based Decision Support System for Electric Arc Furnace (EAF) Online Monitoring and Control
by Bernd Kleimt, Waldemar Krieger, Diana Mier Vasallo, Asier Arteaga Ayarza and Inigo Unamuno Iriondo
Metals 2023, 13(8), 1332; https://doi.org/10.3390/met13081332 - 26 Jul 2023
Cited by 1 | Viewed by 1326
Abstract
In this work, a practical approach for a decision support system for the electric arc furnace (EAF) is presented, with real-time heat state monitoring and control set-point optimization, which has been developed within the EU-funded project REVaMP and applied at the EAF of [...] Read more.
In this work, a practical approach for a decision support system for the electric arc furnace (EAF) is presented, with real-time heat state monitoring and control set-point optimization, which has been developed within the EU-funded project REVaMP and applied at the EAF of Sidenor in Basauri, Spain. The system consists of a dynamic process model based on energy and mass balances, including thermodynamic calculations for the most important metallurgical reactions, with particular focus on the modelling of the dephosphorisation reaction, as this is a critical parameter for production of high-quality steel grades along the EAF process route. A statistical scrap characterization tool is used to estimate the scrap properties, which are critical for reliable process performance and accurate online process control. The underlying process models and control functions were validated on the basis of historical production and measurement data of a large number of heats produced at the Sidenor plant. The online implementation of the model facilitates the accurate monitoring of the process behaviour and can be applied for exact process end-point control regarding melt temperature as well as oxygen, carbon and phosphorus content. Embedded within a model predictive control concept, the model can provide useful advice to the operator to adjust the relevant set-points for energy and resource-efficient process control. Full article
(This article belongs to the Special Issue Digitalization and Advanced Software Support of the Steelmaking Industry)
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19 pages, 2570 KiB  
Article
Development of Three-Dimensional LES Based Meshless Model of Continuous Casting of Steel
by Katarina Mramor, Robert Vertnik and Božidar Šarler
Metals 2022, 12(10), 1750; https://doi.org/10.3390/met12101750 - 18 Oct 2022
Cited by 7 | Viewed by 1391
Abstract
A large-eddy simulation (LES) based meshless model is developed for the three-dimensional (3D) problem of continuous casting (CC) of steel billet. The local collocation meshless method based on radial basis functions (RBF) is applied in 3D. The method applies scaled multiquadric (MQ) RBF [...] Read more.
A large-eddy simulation (LES) based meshless model is developed for the three-dimensional (3D) problem of continuous casting (CC) of steel billet. The local collocation meshless method based on radial basis functions (RBF) is applied in 3D. The method applies scaled multiquadric (MQ) RBF with a shape parameter on seven nodded local sub-domains. The incompressible turbulent fluid flow is described using mass, energy, and momentum conservation equations and the LES turbulence model. The solidification system is solved with the mixture continuum model. The Boussinesq approximation for buoyancy and the Darcy approximation for porous media are used. Chorin’s fractional step method is used to couple velocity and pressure. The microscopic model is closed with the lever rule model. The LES model is compared to the two-equation Low Re k−ε turbulence Reynolds Averaged Navier–Stokes (RANS) model in terms of temperature, velocity and computational times. The LES model resolves transient character of vortices which RANS-type turbulence models are unable to tackle. The computational cost of LES models is considerably higher than in RANS. On the other hand, it results in a much lower computational cost than the direct numerical simulation (DNS). The paper demonstrates the ability of the method to solve realistic industrial 3D examples. Trivial adjustment of nodal densities, high accuracy, and low numerical diffusivity are the main advantages of this meshless method. Full article
(This article belongs to the Special Issue Digitalization and Advanced Software Support of the Steelmaking Industry)
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