Special Issue "Modeling and Simulation of Metallurgical Processes in Ironmaking and Steelmaking"

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Extractive Metallurgy".

Deadline for manuscript submissions: 31 March 2022.

Special Issue Editors

Dr. Thomas Echterhof
E-Mail Website
Guest Editor
Department for Industrial Furnaces and Heat Engineering, RWTH Aachen University, Kopernikustr. 10, 52074 Aachen, Germany
Interests: EAF steelmaking; industrial furnaces; process modelling and simulation; process analysis and optimization
Prof. Dr. Ko-Ichiro Ohno
E-Mail Website
Guest Editor
Department of Materials Science and Engineering, Faculty of Engineering, Kyushu University, 744 Motooka, Nishiku, Fukuoka 819-0395, Japan
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
Dr. Ville-Valtteri Visuri
E-Mail Website
Guest Editor
Process Metallurgy Research Unit, University of Oulu, PO Box 4300, 90014 Oulu, Finland
Interests: hot metal pretreatments; electric arc furnaces; converter metallurgy; ladle metallurgy; continuous casting; process modelling and simulation; kinetics and thermodynamics of metallurgical processes

Special Issue Information

Dear Colleagues,

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
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 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.

Keywords

  • Mathematical modeling
  • Physical modeling
  • Computational fluid dynamics
  • Process metallurgy
  • Ironmaking
  • Steelmaking
  • Data-driven modelling
  • Kinetics
  • Thermodynamics

Published Papers (12 papers)

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Research

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Article
Adaptation of the Rist Operating Diagram as a Graphical Tool for the Direct Reduction Shaft
Metals 2021, 11(12), 1953; https://doi.org/10.3390/met11121953 (registering DOI) - 04 Dec 2021
Viewed by 153
Abstract
The blast-furnace operating diagram proposed by Rist was revised to direct reduction and was specifically applied to the Midrex NGTM process. The use of this graphical tool in the study of an industrial process highlighted the staggered nature of the reduction in [...] Read more.
The blast-furnace operating diagram proposed by Rist was revised to direct reduction and was specifically applied to the Midrex NGTM process. The use of this graphical tool in the study of an industrial process highlighted the staggered nature of the reduction in the shaft furnace with, in particular, the existence of a prereduction zone in the upper part where metallization is thermodynamically impossible. A sensitivity study also showed the impact of the in situ reforming rate on the ability of the gas to completely reduce iron oxides. Finally, we graphically defined the minimum quality required for the top gas to produce direct-reduced iron. Full article
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Article
Experimental and Numerical Investigations on Charging Carbon Composite Briquettes in a Blast Furnace
Metals 2021, 11(11), 1669; https://doi.org/10.3390/met11111669 - 20 Oct 2021
Viewed by 270
Abstract
In the present research, charging carbon composite briquettes (CCB) in a blast furnace (BF) was investigated. The CCB used contained 29.70 wt.% Fe3O4, 39.70 wt.%, FeO, 1.57 wt.% iron, 8.73 wt.% gangue, and 20.30 wt.% carbon. Its reaction kinetics [...] Read more.
In the present research, charging carbon composite briquettes (CCB) in a blast furnace (BF) was investigated. The CCB used contained 29.70 wt.% Fe3O4, 39.70 wt.%, FeO, 1.57 wt.% iron, 8.73 wt.% gangue, and 20.30 wt.% carbon. Its reaction kinetics in BF was examined by nonisothermal tests and modeled. Thereafter, the influence of replacing 10% ore with CCB on BF performance was studied by numerical simulations. Results showed that the CCB reaction behavior in BF could be modeled using the previously proposed model under ags = 1900 m2·m−3. Numerical simulations on a BF with a production of 6250 t hot metal per day (tHM/day) showed that replacing 10% ore with CCB efficiently improved the BF operation for coke saving. In the CCB charging operation, the CCB reached a full iron-oxide reduction above the cohesive zone (CZ) and a carbon conversion of 85%. By charging CCB, the thermal state in the BF upper part was significantly changed while it was not influenced in the BF lower part; the ore reduction was retarded before the temperature reached 1073 K and was prompted after and the local gas utilization tends to increase above the CZ. By the CCB reduction above the CZ, BF top gas temperature was decreased by 8 K, the BF top gas utilization was increased by 1.3%, the BF productivity was decreased by 17 tHM/day, the coke rate was decreased by 52.2 kg/tHM, and ore rate was decreased by 101 kg/tHM. Considering the energy consumption of sintering and coking, charging the CCB could have a significant energy-saving and CO2-emission-reducing effect for BF iron making. Full article
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Article
Thermophysical Model for Online Optimization and Control of the Electric Arc Furnace
Metals 2021, 11(10), 1587; https://doi.org/10.3390/met11101587 - 05 Oct 2021
Viewed by 433
Abstract
A dynamic, first-principles process model for a steelmaking electric arc furnace has been developed. The model is an integrated part of an application designed for optimization during operation of the furnace. Special care has been taken to ensure that the non-linear model is [...] Read more.
A dynamic, first-principles process model for a steelmaking electric arc furnace has been developed. The model is an integrated part of an application designed for optimization during operation of the furnace. Special care has been taken to ensure that the non-linear model is robust and accurate enough for real-time optimization. The model is formulated in terms of state variables and ordinary differential equations and is adapted to process data using recursive parameter estimation. Compared to other models available in the literature, a focus of this model is to integrate auxiliary process data in order to best predict energy efficiency and heat transfer limitations in the furnace. Model predictions are in reasonable agreement with steel temperature and weight measurements. Simulations indicate that industrial deployment of Model Predictive Control applications derived from this process model can result in electrical energy consumption savings of 1–2%. Full article
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Article
Adaptive First Principles Model for the CAS-OB Process for Real-Time Applications
Metals 2021, 11(10), 1554; https://doi.org/10.3390/met11101554 - 29 Sep 2021
Viewed by 391
Abstract
A model-based system for real-time monitoring and operational support has been developed for the Composition Adjustment by Sealed argon-bubbling with Oxygen Blowing (CAS-OB) process. The model of the system is based on a previously developed dynamic model using first principles, i.e., mass and [...] Read more.
A model-based system for real-time monitoring and operational support has been developed for the Composition Adjustment by Sealed argon-bubbling with Oxygen Blowing (CAS-OB) process. The model of the system is based on a previously developed dynamic model using first principles, i.e., mass and energy balances, reaction kinetics, and thermodynamics. Adaptive estimation of state variables has been implemented using a Kalman filter to ensure that the model is able to correct for deviations between measured and calculated temperatures in real-time operation. The estimation technique reduces the standard deviation of the predicted end temperature from 19.5 °C to 5.5 °C in a data series with more than 1000 heats. The system also includes an endpoint optimisation, which calculates the amount of scrap or oxygen to be added to achieve the target temperature at the end of the heat. The model has been implemented in the Cybernetica® CENIT™ framework. The overall model can be regarded as a hybrid digital twin, where a first principles model is adapted in real time using process measurements. The system also includes user interfaces for operators where process predictions can be followed, and suggested optimised inputs are presented. The system has been deployed at two refining stations at SSAB Europe OY in Raahe, Finland. The optimized suggestions for oxygen and scrap are presented to the operators in the graphical user interface. A projected temperature profile is calculated into the near future using the planned operational procedure as well as the projected temperature profile using optimised inputs. Both profiles are displayed in the user interface. Based on these trajectories, the operator can decide on whether to follow the nominal trajectory, or the recommendation from the optimisation This will help the operators make better decisions, which in turn reduces the number of rejected heats in the CAS-OB process. Full article
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Article
Toward a Simplified Arc Impingement Model in a Direct-Current Electric Arc Furnace
Metals 2021, 11(9), 1482; https://doi.org/10.3390/met11091482 - 17 Sep 2021
Viewed by 588
Abstract
A 2D axisymmetric two-phase model was developed to study the effect of an arc impingement on the liquid metal inside an electric arc furnace. In addition to the arc flow dynamics, the model covered the heat transfer and magneto hydrodynamics of the arc [...] Read more.
A 2D axisymmetric two-phase model was developed to study the effect of an arc impingement on the liquid metal inside an electric arc furnace. In addition to the arc flow dynamics, the model covered the heat transfer and magneto hydrodynamics of the arc and the liquid metal. Through a parametric study, three different parameters were considered to predict the most important factors affecting the arc and overall behaviour of the process: the arc gap, the density of the gas, and the total electric current. Understanding the effect of these parameters can show the key factors altering the arc dynamics. The study showed that the total applied current was the most important parameter that influenced the impingement depth and mixing of the liquid metal. The depth of the impingement and strength of the mixing of the liquid bath were directly proportional to the current applied in the furnace. The initial arc gap distance was found to be crucial for sustaining a continuous and stable arc. The value of the gas density was very important for the velocity profile; however, it had no significant effect on the impingement depth. This showed that a constant density could be used instead of a varying gas density with temperature to increase the computational efficiency. The study assessed the effects of the aforementioned factors on the arc impingement depth, velocity magnitude, and arc stability. The conclusions acquired and challenges are also presented. Full article
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Article
Evaluation of Mass Transfer Coefficient during Scrap Melting
Metals 2021, 11(9), 1368; https://doi.org/10.3390/met11091368 - 30 Aug 2021
Viewed by 377
Abstract
Mass transfer is a critical scrap melting step. Herein, mass transfer coefficients (k) during scrap melting were calculated using laboratory-scale experiments. Correlation analysis and the entropy weight method were used to determine the effect of variables on k. The evaluation [...] Read more.
Mass transfer is a critical scrap melting step. Herein, mass transfer coefficients (k) during scrap melting were calculated using laboratory-scale experiments. Correlation analysis and the entropy weight method were used to determine the effect of variables on k. The evaluation model under natural and forced convection was established. It was consistent with the experimental results. Under forced convection, at 1573 and 1673 K, when the rotation speed was increased from 141 to 423 r/min, k increased from 7.50 × 10−5 to 1.54 × 10−4 m/s and from 8.42 × 10−5 to 1.72 × 10−4 m/s, respectively. Furthermore, as the bath temperature was increased from 1573 to 1723 K, the k value of a stationary specimen increased from 3.14 × 10−5 to 5.31 × 10−5 m/s, respectively. Correlation analysis and the entropy weight method indicated that the effects of variables on k decreased as follows: molten pool stirring rate > bath temperature > scrap type. Moreover, the explicit functional relationships between k and the factors affecting k under natural and forced convection conditions were established, and the results were consistent with the experimental data. Our results can be used to determine the quantitative relationships between k and the factors affecting k. Full article
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Article
Application and Evaluation of Mathematical Models for Prediction of the Electric Energy Demand Using Plant Data of Five Industrial-Size EAFs
Metals 2021, 11(9), 1348; https://doi.org/10.3390/met11091348 - 27 Aug 2021
Viewed by 357
Abstract
The electric arc furnace (EAF) represents the most important process route for recycling of steel and the second most productive steelmaking process overall. Considering the large production quantities, the EAF process is subject to continuous optimization, and even small improvements can lead to [...] Read more.
The electric arc furnace (EAF) represents the most important process route for recycling of steel and the second most productive steelmaking process overall. Considering the large production quantities, the EAF process is subject to continuous optimization, and even small improvements can lead to a significant reduction in resource consumption and operating cost. A common way to investigate the furnace operation is through the application of mathematical models. In this study the applicability of three different statistical modeling approaches for prediction of the electric energy demand is investigated by using more than 21,000 heats from five industrial-size EAFs. In this context, particular consideration is given to the difference between linear and nonlinear regression models. Detailed information on the treatment of the process data is provided and the applied methods for regression are described in short, including information on the choice of hyperparameters. Subsequently, the results of the models are compared. Gaussian process regression (GPR) was found to yield the best overall accuracy; however, the benefit of applying nonlinear models varied between the investigated furnaces. In this regard, possible reasons for the inconsistent performance of the methods are discussed. Full article
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Article
Development of a Fast Modeling Approach for the Prediction of Scrap Preheating in Continuously Charged Metallurgical Recycling Processes
Metals 2021, 11(8), 1280; https://doi.org/10.3390/met11081280 - 12 Aug 2021
Viewed by 389
Abstract
Improving the overall energy efficiency of processes is necessary to reduce costs, lower the specific energy consumption and thereby reduce the direct or indirect emission of gases that contribute to climate change. In many metallurgical processes, a large amount of energy is lost [...] Read more.
Improving the overall energy efficiency of processes is necessary to reduce costs, lower the specific energy consumption and thereby reduce the direct or indirect emission of gases that contribute to climate change. In many metallurgical processes, a large amount of energy is lost with the off-gas. In metallurgical recycling processes, off-gas often can be used to preheat the to-be-recycled metal scrap, leading to significantly higher energy efficiency. However, the application of preheating has the disadvantage that it often requires more precise planning in the design and better control of the process. In this paper, a simplified look at a continuously charged scrap preheating aggregate for the widely used electric arc furnace (EAF) in the steel processing industry is used as illustration. Continuous scrap charging in EAF-type furnaces in general has much higher demands on process control and general process knowledge, which is why they are found only very rarely. General issues and basic modeling approaches to mitigate such issues allowing a better process control will be described. In particular, a fast, one-dimensional modeling approach for the determination of the temperature distribution inside a constantly moving scrap bulk, with hot air (or exhaust gases) flowing through it, will be described. Possible modeling applications, assumptions, possible enhancements and limitations are shown. The first results indicate that this approach can be used as a solid basis for the modeling of scrap bulks with thermally thin parts, consisting of materials with similar thermodynamic material properties. Therefore, as a basis, this approach may help improve the design and control of future or existing preheating devices in metal recycling processes. Full article
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Article
Numerical Analysis on Characteristics of Reduction Process within a Pre-Reduction Rotary Kiln
Metals 2021, 11(8), 1180; https://doi.org/10.3390/met11081180 - 25 Jul 2021
Viewed by 494
Abstract
The reduction process inside the ore pre-reduction rotary kiln involves a series of physicochemical reactions, and in-depth understanding of the reduction behavior is helpful to improve the product quality and productivity. This paper reports a three-dimensional steady state mathematical model based on computational [...] Read more.
The reduction process inside the ore pre-reduction rotary kiln involves a series of physicochemical reactions, and in-depth understanding of the reduction behavior is helpful to improve the product quality and productivity. This paper reports a three-dimensional steady state mathematical model based on computational fluid dynamics, which considers heat transfer, mass transfer and chemical reactions inside the rotary kiln. A user-defined functions (UDFs) program in C language is developed to define physical parameters and chemical reactions, and calculate the heat and mass transfer between freeboard and bed regions. The model is validated by measurement data and is then used to investigate the detailed information inside the rotary kiln. The results show that there is a temperature gradient in the bed, which is maximal near the kiln tail and decreases gradually as the reduction process progresses. The result also confirms that the reduction of FeO to Fe is the limiting step of the whole reduction process because this reaction requires a higher reduction potential. Furthermore, the influence of C/O mole ratio and fill degree are analyzed by comparing the average bed temperature, reduction potential and metallization ratio. Full article
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Article
Towards Greener Industry: Modelling of Slag Heat Recovery
Metals 2021, 11(7), 1144; https://doi.org/10.3390/met11071144 - 20 Jul 2021
Viewed by 993
Abstract
The steel industry, in accordance with the momentum of greener industry, has welcomed the changes and is actively pursuing that objective. One such activity is the commitment to energy recovery from by-products such as slag since the average energy content of ferrous slags [...] Read more.
The steel industry, in accordance with the momentum of greener industry, has welcomed the changes and is actively pursuing that objective. One such activity is the commitment to energy recovery from by-products such as slag since the average energy content of ferrous slags is around 1 to 2 GJ/tslag. The recovered energy could, then, be used in heating or drying process among others. The RecHeat was designed and modelled iteratively to achieve an optimised heat recovery apparatus. The model shows that the temperature of different sections of the heat exchanger part varies from 170 to 380 °C after slag pouring while the average air temperature at the entrance of the heat exchanger is less than 150 °C. Furthermore, the temperature of the fluid medium changes from 125–140 °C to 260–340 °C from one end of the heat exchanger part to the other at the end of the simulation. The outlet temperature at the end of the simulation is calculated to be around 340 °C, which shows an increase by at least 200 °C in the temperature of the air entering the apparatus. Full article
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Review

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Review
Mathematical Methodology and Metallurgical Application of Turbulence Modelling: A Review
Metals 2021, 11(8), 1297; https://doi.org/10.3390/met11081297 - 17 Aug 2021
Cited by 1 | Viewed by 409
Abstract
This paper focusses on three main numerical methods, i.e., the Reynolds-Averaged Navier-Stokes (RANS), Large Eddy Simulation (LES), and Direct Numerical Simulation (DNS) methods. The formulation and variation of different RANS methods are evaluated. The advantage and disadvantage of RANS models to characterize turbulent [...] Read more.
This paper focusses on three main numerical methods, i.e., the Reynolds-Averaged Navier-Stokes (RANS), Large Eddy Simulation (LES), and Direct Numerical Simulation (DNS) methods. The formulation and variation of different RANS methods are evaluated. The advantage and disadvantage of RANS models to characterize turbulent flows are discussed. The progress of LES with different subgrid scale models is presented. Special attention is paid to the inflow boundary condition for LES modelling. Application and limitation of the DNS model are described. Different experimental techniques for model validation are given. The consistency between physical experimentation/modelling and industrial cases is discussed. An emphasis is placed on the model validation through physical experimentation. Subsequently, the application of a turbulence model for three specific flow problems commonly encountered in metallurgical process, i.e., bubble-induced turbulence, supersonic jet transport, and electromagnetic suppression of turbulence, is discussed. Some future perspectives for the simulation of turbulent flow are formulated. Full article
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Review
A Review of Bubble Dynamics in Liquid Metals
Metals 2021, 11(4), 664; https://doi.org/10.3390/met11040664 - 19 Apr 2021
Cited by 4 | Viewed by 757
Abstract
Gas bubbles are of major importance in most metallurgical processes. They promote chemical reactions, homogenize the melt, or float inclusions. Thus, their dynamics are of crucial interest for the optimization of metallurgical processes. In this work, the state of knowledge of bubble dynamics [...] Read more.
Gas bubbles are of major importance in most metallurgical processes. They promote chemical reactions, homogenize the melt, or float inclusions. Thus, their dynamics are of crucial interest for the optimization of metallurgical processes. In this work, the state of knowledge of bubble dynamics at the bubble scale in liquid metals is reviewed. Measurement methods, with emphasis on liquid metals, are presented, and difficulties and shortcomings are analyzed. The bubble formation mechanism at nozzles and purging plugs is discussed. The uncertainty regarding the prediction of the bubble size distribution in real processes is demonstrated using the example of the steel casting ladle. Finally, the state of knowledge on bubble deformation and interfacial forces is summarized and the scalability of existing correlations to liquid metals is critically discussed. It is shown that the dynamics of bubbles, especially in liquid metals, are far from understood. While the drag force can be predicted reasonably well, there are large uncertainties regarding the bubble size distribution, deformation, and lift force. In particular, the influence of contaminants, which cannot yet be quantified in real processes, complicates the discussion and the comparability of experimental measurements. Further open questions are discussed and possible solutions are proposed. Full article
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Planned Papers

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.

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