Electric Arc Furnace Operation Research Articles

Prediction interval soft sensor for dissolved oxygen content estimation in an electric arc furnace

In this study, a novel soft sensor modeling approach using Takagi–Sugeno (TS) fuzzy models and Prediction Intervals (PIs) is presented to quantify uncertainties in Electric Arc Furnace (EAF) steel production processes, namely to estimate the dissolved oxygen content in the steel bath. In real EAF operation, dissolved oxygen content is measured only a few times in the refining stage; therefore, the approach addresses the challenge of predicting unobserved output under conditions of irregular and scarce output measurements, using two distinct methods: Instant TS (I-TS) and Input Integration TS (II-TS). In the I-TS method, the model is computed for each individual indirect measurement, while the II-TS approach integrates these indirect measurements. The inclusion of PIs in TS models allows the derivation of the narrowest band containing a prescribed percentage of data, despite the presence of heteroscedastic noise. These PIs provide valuable insight into potential variability and allow decision-makers to evaluate worst-case scenarios. When evaluated against real EAF data, these methods were shown to effectively overcome the obstacles posed by scarce output measurements. Despite its simplicity, the I-TS model performed better in terms of interpretability and robustness to the operational reality of the EAF process. The II-TS model, on the other hand, showed excellent performance on all metrics but exhibited theoretical inconsistencies when deviating from typical operations. In addition, the proposed method successfully estimates carbon content in the steel bath using the established dissolved oxygen/carbon equilibrium, eliminating the need for direct carbon measurements. This shows the potential of the proposed methods to increase productivity and efficiency in the EAF steel industry.

The Development of Simulation and Optimisation Tools with an Intuitive User Interface to Improve the Operation of Electric Arc Furnaces

The paper presents a novel decision support system designed to improve the efficiency and effectiveness of decision-making for electric arc furnace (EAF) operators. The system integrates two primary tools: the EAF Simulator, which is based on advanced mechanistic models, and the EAF Optimiser, which uses data-driven models trained on historical data. These tools enable the simulation and optimisation of furnace settings in real time and provide operators with important insights. A key objective was to develop a user-friendly interface with the Siemens Insights Hub Cloud Service and Node-RED that enables interactive management and support. The interface allows operators to analyse and compare past and simulated batches by adjusting the input data and parameters, resulting in improved optimisation and reduced costs. In addition, the system focuses on the collection and pre-processing of input data for the simulator and optimiser and uses Message Queuing Telemetry Transport (MQTT)communication between the user interfaces and models to ensure seamless data exchange. The EAF Simulator uses a comprehensive mathematical model to simulate the complex dynamics of heat and mass transfer, while the EAF Optimiser uses a fuzzy logic-based approach to predict optimal energy consumption. The integration with Siemens Edge Streaming Analytics ensures robust data collection and real-time responsiveness. The dual-interface design improves user accessibility and operational flexibility. This system has significant potential to reduce energy consumption by up to 10% and melting times by up to 15%, improving the efficiency and sustainability of the entire process.

Application of an Artificial Neural Network for Efficient Computation of Chemical Activities within an EAF Process Model

The electric arc furnace (EAF) is considered the second most important process for the production of crude steel and is usually used for the melting of scrap. With the current emphasis on defossilization, its share in global steelmaking is likely to further increase. Due to the large production quantities, minor improvements to the EAF process can still accumulate into a significant reduction in overall energy and resource consumption. A major aspect in the efficient operation of the EAF is achieving beneficial slag properties, as the slag influences the composition of the steel and can reduce energy losses as well as the maintenance cost. In order to investigate the EAF operation, a dynamic process model is applied. Within the model, the chemical reactions of the metal–slag system are calculated based on the activities of the involved species. In this regard, multiple models for the calculation of the chemical activities have been implemented. However, depending on the chosen model, the computation of the slag activities can be computationally demanding. For this reason, the application of a neural network for the calculation of the chemical activities within the slag is investigated. The performance of the neural network is then compared to the results of the previously applied models by using the commercial software FactSage as a reference. The validation shows that the surrogate model achieves great accuracy while keeping the computation demand low.

Improvement of the control system with the drive of high voltage arc furnaces

The article presents a method for improving the control system of electric motors driving the electrodes of high-voltage arc furnaces. The developed method today in the field of metallurgy is of great importance in solving problems such as at the first stage of melting and casting metal in arc steelmaking furnaces, when the electrodes are initially lowered into the working bath, the connection point breaks as a result of contact with a metal plate, the incompatibility of the formation of an electric arc on the electrodes, holding the electrodes at a certain distance from the metal charge. Also, automatic control of the speed of movement of the electrodes, i.e. (the time of formation of an electric arc in the electrodes, which reduces the speed of its movement, the time of lifting the electrodes, lowering, increasing the speed of movement), is relevant for solving problems such as welding electrodes into a metal shackle. The main purpose of introducing this method into practice is to reduce electricity consumption and ensure reliable operation of high-voltage electric arc furnaces by reducing the melting time of metal.

Impact of Injection Rate on Flow Mixing during the Refining Stage in an Electric Arc Furnace

During the refining stage of electric arc furnace (EAF) operation, molten steel is stirred to facilitate gas/steel/slag reactions and the removal of impurities, which determines the quality of the steel. The stirring process can be driven by the injection of oxygen, which is carried out by burners operating in lance mode. In this study, a computational fluid dynamics (CFD) platform is used to simulate the liquid steel flow dynamics in an industrial-scale scrap-based EAF. The CFD platform simulates the three-dimensional, transient, non-reacting flow of the liquid steel bath stirred by oxygen injection to analyze the mixing process. In particular, the CFD study simulates liquid steel flow in an industrial-scale EAF with three asymmetric coherent jets, which impacts the liquid steel mixing under different injection conditions. The liquid steel mixing is quantified by defining two variables: the mixing time and the standard deviation of the flow velocity. The results indicate that the mixing rate of the bath is determined by flow dynamics near the injection cavities and that the formation of very low-velocity regions or ‘dead zones’ at the center of the furnace and the balcony regions prevents flow mixing. This study includes a baseline case, where oxygen is injected at 1000 SCFM in all the burners. Two sets of cases are also included: The first set considers cases where oxygen is injected at a reduced and at an increased uniform flow rate, 750 and 1250 SCFM, respectively. The second set considers cases with non-uniform injection rates in each burner, which keep the same total flow rate of the baseline case, 3000 SCFM. Comparison between the two sets of simulations against the baseline case shows that by increasing the uniform flow rate from 1000 to 1250 SCFM, the mixing time is reduced by 10.9%. Moreover, all the non-uniform injection cases reduce the mixing time obtained in the baseline case. However, the reduction in mixing times in these cases is accompanied by an increase in the standard deviations of the flow field. Among the non-uniform injection cases, the largest reduction in mixing time compared to the baseline case is 10.2%, which is obtained when the largest flow rates are assigned to coherent jets located opposite each other across the furnace.

Evaluation of copper slag and stainless steel slag as replacements for blast furnace slag in binary and ternary alkali-activated cements

Commonly used alkali activation precursors such as blast furnace slag and fly ash will soon become less available due to resource competition, and may cease to be produced in certain regions. This limitation in future supply is a main driving force for the investigation of alternative precursor sources, such as non-blast furnace slags and non-ferrous slags, to produce alkali-activated binders. The current study investigates the incorporation of copper slag (CS) and stainless steel slag resulting from electric arc furnace operations (EAFSS) as partial replacements for ground granulated blast furnace slag (GGBFS) in producing alkali-activated materials (AAMs), at paste level. Five binary alkali-activated mixtures with different replacement levels of GGBFS with CS, and three ternary mixtures with both CS and EAFSS as partial and total replacements for GGBFS, are activated by a sodium silicate solution. Replacing GGBFS with CS and EAFSS retards the reaction kinetics, resulting in improved fresh-state properties of the investigated AAMs, better retention of workability and longer setting times. The reaction of alkali-activated 100% CS shows minimal initial exothermic activity until 3.5 h, when a single intense peak appears, representing delayed dissolution and subsequent polycondensation. X-ray diffraction (XRD) data indicate that the main crystalline phases of CS and EAFSS are stable in these alkaline systems; it is the glassy components that react. The use of CS and EAFSS in blended AAMs causes a minor increase in porosity of ~ 1–3% with respect to GGBFS only, and a small reduction in compressive and flexural strengths, although these reach 80 MPa and 8 MPa, respectively, after 28 days, even at a replacement level over 65 wt. %. Conversely, the 100% CS mixture exhibits a one-day compressive strength of 23 MPa, with a negligible increase thereafter. This result agrees with both FTIR and SEM analysis which highlight only minor changes in binder development after two days. It is believed that the unusual behaviour of CS in the investigated mixtures is related to the low availability of calcium in this precursor material.

An energy optimization study of the electric arc furnace from the steelmaking process with hot metal charging

A study of energy optimization derived from simulation software has been done for the high-intensity energy of an Electric Arc Furnace (EAF), hot metal charging within the cooler duct dedusting system. The present work aims to develop a dynamic model of the EAF operation based on mass and energy balances integrated with simulation of the dedusting system with hot metal charging system using MATLAB® and Computational Fluid Dynamics (CFD). The effect of various percentages of hot metal charging on EAF performance and the dedusting system was simulated and validated from the real EAF data plant from one of the steel manufacturer companies in Indonesia. Three cases for the EAF with various hot metal (HM), sponge iron, and scrap iron charging compositions and four cases for the EAF with post-combustion CO have been developed. Careful observation shows that the electric arc power consumption can be reduced down to 72.9 MWh from 87.4 MWh (ca. 16% more efficient) while at the same time increasing the HM charging temperature at the endpoint of the duct dedusting system up to 900°C from 540°C (app. 65% higher). Additionally, advanced simulation of an EAF with post-combustion CO shows that power consumption can be decreased to 59.9 MWh (ca. 30% more efficient).

Effects of EAF Operations on Water‐Cooling Panel Overheating

An electric arc furnace (EAF) is a furnace that utilizes mainly electric energy to melt scraps into liquid. Above the liquid steel is called the freeboard, and combustion that happens here can significantly heat the furnace wall, resulting in overheating issues on water‐cooling panels. In this study, a comprehensive computational fluid dynamics (CFD) model is developed to predict the side wall temperature distribution of an EAF. The comprehensive CFD model is integrated into a coherent jet, a DC electric arc, and a slag foaming models.

Arc Voltage Distortion as a Source of Higher Harmonics Generated by Electric Arc Furnaces

Due to high unit capacities, electric arc furnaces are among the receivers that significantly affect the power system from which they are supplied. Arc furnaces generate a number of disturbances to the power grid, including fast-changing voltage fluctuations causing the phenomenon of flickering light, asymmetry, and deformation of the voltage curve. The main issues discussed in the article are problems related to the distortion of current and voltage waveforms, resulting from the operation of electric arc furnaces. An analysis of the indices characterizing the voltage distortion recorded in the supply network of the arc furnaces is presented. The changes in the range of current and voltage waveform deformation in individual smelting phases in the arc furnace are also presented. Furthermore, the changes in the degree of deformation of the current and voltage waveforms in the individual smelting phases in an arc furnace are presented. A multi-voltage electric arc model used in computer simulations is proposed.

Data-Driven Modelling and Optimization of Energy Consumption in EAF

In the steel industry, the optimization of production processes has become increasingly important in recent years. Large amounts of historical data and various machine learning methods can be used to reduce energy consumption and increase overall time efficiency. Using data from more than two thousand electric arc furnace (EAF) batches produced in SIJ Acroni steelworks, the consumption of electrical energy during melting was analysed. Information on the consumed energy in each step of the electric arc process is essential to increase the efficiency of the EAF. In the paper, four different modelling approaches for predicting electrical energy consumption during EAF operation are presented: linear regression, k-NN modelling, evolving and conventional fuzzy modelling. In the learning phase, from a set of more than ten regressors, only those that have the greatest impact on energy consumption were selected. The obtained models that can accurately predict the energy consumption are used to determine the optimal duration of the transformer profile during melting. The models can predict the optimal energy consumption by selecting pre-processed training data, where the main steps are to find and remove outlier batches with the highest energy consumption and identify the influencing variables that contribute most to the increased energy consumption. It should be emphasised that the electrical energy consumption was too high in most batches only because the melting time was unnecessarily prolonged. Using the proposed models, EAF operators can obtain information on the estimated energy consumption before batch processing depending on the scrap weight in each basket and the added additives, as well as information on the optimal melting time for a given EAF batch. All models were validated and compared using 30% of all data, with the fuzzy model in particular providing accurate prediction results. It is expected that the use of the developed models will lead to a reduction in energy consumption as well as an increase in EAF efficiency.

Statistical Models of EAF Harmonics Developed for Harmonic Estimation Directly From Waveform Samples Using Deep Learning Framework

In this research work, a deep learning (DL)-based method for the fast and accurate analysis of current harmonics of electric arc furnaces (EAF) is proposed. For such a system, a large amount of EAF current data is required for the training phase of the DL-based structure, which is not only a thorny but also an expensive procedure. Hence, the second focus of this research work is to gain the ability to generate EAF currents with realistic harmonic contents based on a much smaller amount of field data of EAF currents. For this purpose, EAF current data, recorded at a transformer substation supplying an EAF plant during a tap-to-tap time of the EAF operation, are examined in terms of harmonic component amplitudes and phases. Then, a significantly larger amount of EAF current data is regenerated based on the statistics of current harmonics mimicking the real EAF behavior and this synthetic data are used to train the DL-based harmonic estimator. This estimator is able to estimate both amplitudes and phases of the harmonics without computing any time- or frequency-domain features during the estimation process. Hence, the outcomes of this research work are twofold: First, detailed analysis of the EAF current harmonic behavior is achieved, which reveals the operation principles of the EAF. Second, a DL-based harmonic estimator is trained, which is able to output the amplitude and phase estimations directly out of waveform samples without any feature extraction. The proposed system aims to serve the needs of active power filters of the EAF installations in the electricity system, since it has been shown that fast and accurate harmonic amplitude and phase estimations are obtained.

Generative adversarial network–based real-time temperature prediction model for heating stage of electric arc furnace

For accurately predicting the molten steel temperature of heating stage in electric arc furnace (EAF) in real time, a novel prediction model based on the generative adversarial network (GAN) is proposed in this paper. First, the generator is specially designed based on the simplified energy balance of molten steel combined with long short-term memory (LSTM) network. The sequential smelting variables are used as the input of generator, which is an effective representation of the time-variant EAF operations. Meanwhile, the discriminator is established to indicate the deviation of the changing trend between the generator predicted temperature and the simulated temperature. Here, the simulated temperature is produced according to smelting experience which is a good supplement to the sparse temperature measurements. Subsequently, the loss function of the generator is improved to consider both the accuracy of predicted temperature and the correctness of temperature changing trend. Through alternate training the discriminator and generator, the generator is finally able to predict the temperature of molten steel in real time with a better precision. Experiments with practical data verify the effectiveness of the proposed model.

Benchmarking of FeNi Electric Arc Furnace Operations for Developing Furnace Design Parameters for Lateritic Ores in the Intermediate SiO2/MgO Range of between 2.2 and 2.7

An extensive benchmarking study was conducted to establish the design for a circular 3-electrode electric furnace of 55MW for smelting of nickel saprolitic laterite ores in the intermediate SiO2/MgO range of 2.2-2.7. The investigation was focused on “intermediate slags” in the compositional range of 15-25% FeO, 0-5% Al2O3 and a SiO2/MgO ratio of 2.2-2.7. Industrial furnace references, operating at SiO2/MgO ratios of 2.7, were reviewed as basis for the design. It was found that slag superheats of up to 200°C is needed when producing high grade ferronickel. The bath resistance was estimated to 9-18mOhm and the operational resistance at shielded arc conditions to 35-55mOhm (Parc/Pbath ratios of 2.0-3.0), corresponding to a secondary voltage in the range of 1400-1750V. From the risk mitigation point of view due to slag characteristics, the furnace design was not limited to operation in shielded arc mode solely, but also towards brush arc operation. At the selected hearth power density of ~200 kW/m2 a wide operation window (Parc/Pbath ratio 1.0-2.6) was found manageable to realize freeze lining control at the estimated peak sidewall heat flux of maximum 165kW/m2. A fit for purpose conservative design approach was defined, aligned with industrial references as well as the latest furnace technology developments in intensive copper cooling elements and power stabilization system, which provides a sound basis for the proposed approach.

The Influence of Electric-Arc-Furnace Input Feeds on its Electrical Energy Consumption

Operation of the electric arc furnaces (EAFs) is a subject to consider fluctuations in terms of its key performance indicators, such as the electrical energy consumption (EEC), tap-to-tap time, steel yield, and others. In this paper, a more detailed analysis of the electric arc furnace data is performed, investigating its EEC. It is well known that the EEC is affected by the weight and the type of charged scrap, the operational delays, and the tapping temperature. On the other hand, one can also deduce that the feeds, such as the carbon and the oxygen, could also affect the EEC, due to their role in redox equations and impact to the bath energy balance. Therefore, special attention is devoted to the analysis of the carbon-to-oxygen ratio during the electric arc furnace operation and the consequent influence of the oxygen availability on the EEC. Statistical analysis of more than 2500 heats of data, which were clustered according to the produced steel grade and the charged scrap mixture, has revealed that besides the beforementioned factors, fluctuations in EEC could appear also due to different amounts of added carbon and oxygen. Since the furnace operators usually rely on predefined guidelines and their own experience when actuating the furnace, a simplistic statistical approach can be used to reveal some of the weaknesses in the control routines, which can be used as a starting point to improve their actuation, leading to decreased energy consumption.Graphical

Harmonic-based thermal analysis of electric arc furnace's power cables considering even current harmonics, forced convection, operational scheduling, and environmental conditions

The thermal analysis of power cables to improve their lifetime and reliability is essential. Also, the current harmonics intensify the excessive heat condition of power cables. Although several research works have been introduced for harmonic-based thermal analysis of power cables, there is a research gap about developing a thermal analysis of power cable focusing on the even current harmonics. This paper tries to such a research gap by proposing a new harmonic-based thermal analysis of electric arc furnace (EAF) power cables, which considers even harmonics. The impacts of ambient conditions and the performance of air-forced ventilation of cable tunnels are studied in this article. The proposed method is applied to an actual steel company located in Iran. The simulation test results based on the finite element method (FEM) in COMSOL Multiphysics are compared with actual measured values to validate the proposed method. Test results illustrate the advantages of the proposed method. Moreover, different paradigms for the cyclic operation of EAFs on the thermal behavior of power cables have been analyzed. Simulation results infer that 4.8% inaccuracy (about 3 ̊C) occurs in the thermal analysis due to neglecting the current harmonics. In addition, approximately 41% improvement is achievable by appropriate forced convection. Test results also imply that the maximum temperature of power cables could be effectively controlled by changes in the operation and scheduling of EAFs. Around 10% decrement in the cable system temperature could be obtained by 10 min increment time interval between two operating cycles of the EAF. Simulation results illustrate the advantages of the proposed study.

Fuel-melting aggregate “MAGMA-1” for processing wastes of electric steelmaking

Toughening of ecological requirements to metallurgical production and economic factors, stipulated by competitiveness at the metal products market, increased actuality of utilization of slags, sludges and dust, obtained in steelmaking. It was shown, that from the economic point of view, processing of slags obtained at operation of modern ultra-powerful electric arc furnaces (EAF), is the item of highest interest. At present, their processing is restricted often by maximum possible recovery of metallic inclusions. At that the problem of utilization slag remains, dust and sludges remains unsolved. A pyrometallurgical method of electric steelmaking slags processing in EAF was considered. The process involves application of liquid phase reducing of metal oxides and correction of slag composition aimed at obtaining cast slag marketable products and clinker for cement industry. Data of test heats for slags utilization at the 5-ton EAF presented. The proposed technology enables to make industrial scale processing of slags with obtaining cast iron and melted clinker. Taking into consideration that EAF coefficient of efficiency, accounting fuel consumption for electrical energy obtaining is not high and is 23–24%, it was proposed to do slags processing in fuel-melting aggregates, in which the efficiency of primary energy utilization is higher (efficiency coefficient is 25–40%). The firm OJSC NTP “Akont” developed a technical project of a fuel-melting aggregate “MAGMA-1” for processing electric steelmaking slags. Its basic technical data and performance indices presented. It was shown that additional decrease of fuel consumption can be provided at utilization of liquid slag tapped out of EAF in a course of a heat, as well as at charge heating in a tubular rotating heater by waste gases.

Caracterización y simulación de fenómenos dinámicos relacionados con la actuación de un horno de arco eléctrico conectado al sistema eléctrico de potencia de Ecuador

La operación de los hornos de arco eléctrico (EAF) causa variaciones en las variables eléctricas, como son: potencia activa, reactiva, voltaje y frecuencia. Estas variaciones se deben al funcionamiento propio del EAF que produce un consumo altamente intermitente, lo que lo hace una carga especial. Este trabajo presenta una metodología para el análisis e interpretación de los datos registrados por el sistema SCADA/EMS y el sistema de monitoreo de área extendida WAMS, durante la operación de un EAF conectado al sistema eléctrico de potencia (SEP) de Ecuador. El propósito de este análisis es caracterizar las perturbaciones a través de las variables eléctricas frecuencia, voltaje, potencia activa y reactiva, medidas en la Subestación más sensible a la operación del EAF. Para esto, se realiza un análisis de datos multivariantes basado en Funciones Empíricas Ortogonales EOF, junto con clasificadores inteligentes. Adicionalmente, se propone un modelo de EAF para simulación de transitorios electromecánicos, el cual se basa en un tren de pulsos de ancho modulado (Pulse-width modulation PWM), afectado por una señal de ruido de frecuencia parametrizable. Tanto la propuesta metodología para caracterización de la actuación del EAF a través de registros, como el modelo propuesto muestran prometedores resultados respecto de la evaluación post-operativa y de planeamiento operativo del impacto de la operación de un EAF en el SEP.

Foamy slag practice to enhance the energy efficiency of electric arc furnace: An industrial scale validation

The aim of the present work is to demonstrate the importance of foamy slag practice during steelmaking in electric arc furnaces (EAF). Steel making practices with foamy slag in the EAF not only reduces the energy consumption but it also protects the refractory materials of furnace linings from the high energy intensity generated by electrodes. It also decreases the noise pollution in conjunction with reduced melting time during the process. Foamy slag practices in electric arc furnace operations persists since long in steel making industries but here authors tried to modify the aforesaid practice at the concern plant in most economical way without impairing the steel quality.In the present study a process-controlled feeding of fine metallurgical coke for optimized foaming has been developed and simultaneous increment in energy efficiency has been noticed by collecting and analyzing the process related data. To carry out the present study more than 140 heats have been taken into consideration, with and without foamy slag generation in electric arc furnace. Considerable reduction in the electrical energy consumption (i.e. from ∼510 kwh per tone to ∼450 to 460 kwh per tone) has been noticed along with the reduction in total melting time from ∼87 min to ∼76 min. Present work also confirms that optimized met coke injection in the range of 15–18 kg/tone certainly helps in increasing the foaminess of the slag. FeO content of the slag was also assessed and found in the range of 17–21 wt% having the basicity of 1.8–2.2, which is a good agreement in prospects of yield as well as quality of the steel.

Power System Impedance Estimation Using a Fast Voltage and Current Changes Measurements

Equivalent power system impedance is an important electrical quantity from many points of view. Areas in which this parameter plays an important role include, in particular: Voltage stability analysis, power quality, or fault condition analysis. Power system impedance estimation in real operation conditions can be performed by one of the non-invasive methods described by different authors. This paper aims to investigate and compare seven different methods for power system impedance estimation based on voltage and current variations measurement. After a brief description of selected methods, these methods were applied for power system impedance estimation in the case of two simple simulation tests and then in the case of three real measured data. Voltage and current changes used for power system impedance estimation in real conditions were measured in high voltage (HV) and medium voltage (MV) substations feeding steel mill with the electric arc furnace (EAF) operation. As the results presented in this paper have shown, not all of the methods analyzed are suitable for determining the power system impedance based on the fast step changes of voltage and current that occur, for example, during an EAF operation. Indeed, some of the tested methods were originally designed to determine the power system impedance from changes in voltages and currents recorded at steady state.

Energy efficient solutions of DC Electric Arc Furnace Bottom Electrode

Analysis of recent research and publications. The problems of thermal state of billet-type bottom electrode (BE) in liquid bath of DC electric arc furnace (EAF) are associated with limited thermal conductivity of the rod in the absence of available alternative to copper-steel pair. There isn’t enough data on values of convective and Joule components of heat flux, passing through BE, initiated by electrovortex flows (EVF); regarding influence of thermophysical characteristics of transition copper-steel zone and cooling rate of BE copper part on the position of phase transition surface of steel part. Problems are considered, especially related to innovative “flat bath” steelmaking technologies, in which the possibilities of dead time pauses for periodic renewal of the BE body due to “EVF-off” are significantly limited.Purpose. To investigate the effect of EVT on the thermal state of bottom electrode and to develop on this base the energy efficient BE solutions.Methodology. Numerical simulations of BE thermal state and an industrial testing of essentials. Findings & Originality. For the first time, the joint effect of EVF, Joule heat, and characteristics of BE transition zone copper – steel on the position and equilibrium thickness of solid steel part of the BE, which determines the energy efficiency of DC EAF operation, was comprehensively studied.Research implications. Numerical simulations of EVF in DC EAF steelmaking bath and heat transfer with a phase transition through BE were carried out. The heat flux density and local EVF velocity in anode well are 1.8–2 MW/m2 and 0.75 m/s, respectively. Equilibrium thickness of solid steel BE part is critically dependent on the width of copper-steel transition zone, should not exceed 20-25 mm. Contribution of Joule component of total thermal load on the BE does not exceed 20%. Intensification of cooling rate above ≥ 20 kW/(m2K) practically doesn’t affect the BE solid steel part thickness.Practical implications. Manufacturing technology of BE with a narrow transition zone by the method of two-stage electro-slag welding of copper on a steel billet, which ensures stable DC EAF operation and increases energy efficiency, has been improved.