Special Issue "Modeling and Simulation of Metallurgical Processes in Ironmaking and Steelmaking"
Deadline for manuscript submissions: 31 March 2022.
Interests: EAF steelmaking; industrial furnaces; process modelling and simulation; process analysis and optimization
Interests: blast furnace; iron ore sintering; iron ore granulation; iron ore reduction reaction; self-reducing pellet; carbothermic reduction; cohesive zone; iron carburization behavior; iron ore softening and melting behavior
Special Issues, Collections and Topics in MDPI journals
Interests: hot metal pretreatments; electric arc furnaces; converter metallurgy; ladle metallurgy; continuous casting; process modelling and simulation; kinetics and thermodynamics of metallurgical processes
The UN’s 2030 Sustainable Development Goals, the Paris Agreement, and the European Green Deal, among other goals, all aim to improve the sustainability of industrial production and to reduce CO2 emissions. Europe, for example, aims to reach carbon neutrality and a circular economy by 2050. This goal cannot be achieved without the ironmaking and steelmaking industries.
To reach this goal, further process optimizations with regard to energy and resource efficiency, as well as the development of new processes or process routes, are needed. However, the parameters necessary for the analysis and optimization of the existing and new metallurgical processes in ironmaking and steelmaking often cannot be measured directly because of the harsh conditions inside the furnaces and metallurgical vessels.
Typically, the direct information sources in ironmaking and steelmaking are off-gas analysis and spot measurements, for which a delay for the analysis of the sample must be reserved. Owing to the harsh environment, possibilities to determine the flow conditions in the vessels by measurements are even more limited.
While new methods for the direct and continuous measurement of some of these parameters are currently under development, for many processes they are not available at this time. Furthermore, plant trials that would be necessary to evaluate the impact of different optimization strategies may be impossible because of the prohibitive cost or safety concerns in many cases.
Modeling and simulation have thus established themselves as an invaluable source of information regarding otherwise unknown process parameters, and as an alternative to plant trials with a lower associated cost, risk, and duration. Models are also applicable for model-based control of metallurgical processes.
In this Special Issue on “Modeling and Simulation of Metallurgical Processes in Ironmaking and Steelmaking”, we aim to collect regular and review articles to showcase the recent advances in the modeling and simulation of unit processes in ironmaking and steelmaking, while considering the latest experimental results and process operational data. We also encourage studies that examine the integration of process models to simulate process chains.
Dr. Thomas Echterhof
Prof. Ko-Ichiro Ohno
Dr. Ville-Valtteri Visuri
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 papers will be 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 1800 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.
- Mathematical modeling
- Physical modeling
- Computational fluid dynamics
- Process metallurgy
- Data-driven modelling
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Data-driven modelling and optimization of energy consumption in EAF
Authors: Simon Tomažič; Goran Andonovski; Igor Škrjanc; Vito Logar
Affiliation: University of Ljubljana, Faculty of Electrical Engineering, Tržaška 25, 1000 Ljubljana, Slovenia
Abstract: In the steel industry, optimization of production processes has in recent years gained increasing attention. Large amounts of historical data and various machine learning methods can be used to reduce energy consumption and increase overall time efficiency. Using data of more than two thousand electric arc furnace (EAF) batches produced in SIJ Acroni steelworks, the consumption of electrical energy during melting was analyzed. Information about the consumed energy within each individual electric arc operation step is essential in order to achieve higher EAF efficiency. The paper will present three different modeling approaches for prediction of the electrical energy consumption during the EAF operation: nonlinear regression, evolving modeling and discriminant analysis combined with clustering. In the learning phase from a set of more than ten regressors, only those with the greatest influence on energy consumption (e.g., total weight of the scrap, injected oxygen, added carbon) were selected. The obtained models, which can predict optimal energy consumption, are used to determine the transformer tap, i.e., electrical power, during melting. Together with the classification of batches according to charging recipes and melting programs, several individual models have been developed, which describe the consumption of energy for each group and enable determination of the optimal melting profiles. The models can predict the optimal energy consumption due to the selection of the training data, i.e., finding and using outlier batches with the highest energy consumption and identifying the influencing variables, which contribute the most to the increased energy consumption. Using the proposed models, EAF operators can get the information on estimated energy consumption prior to batch processing, depending on the combination of added materials (scrap composition), as well as the information on optimal melting program to be used for a certain EAF charge. All models were validated and compared using 30 % of all data. It is expected that usage of the developed models will lead to reduced energy consumption, as well as to an increase of the EAF efficiency.