Current Electric Arc Furnace Research Articles

Melt azimuthal rotation in direct current electric arc furnace without external axial magnetic field

This paper considers the azimuthal rotation of melt about the vertical axis of a DC EAF, which is ensured by an inclined installation of the power supply electrodes, which is a patented solution that is the basis for this article. Near inclined electrodes, the Lorentz force has a pronounced azimuthal component, which is the driver of the melt rotation without an external axial magnetic field – thus, additional energy consumption to create an external magnetic field is not necessary. The main design and technological solutions, formulated in the patent and presented by the authors, were obtained using observations and numerical LES study for a laboratory-scale experimental setup (capacity 4.8 kg of GaInSn), as well as using LES computations for an industrial-scale DC EAF (capacity 3.6 t of molten steel). Basic technological solutions studied: the flow of the melt may be controlled by varying the number of vertical and inclined electrodes of DC EAF, choosing the angle of inclination of the electrodes as well as choosing the sequence of turning on and turning off power supply through the electrodes.

Numerical modelling of heating and melting of metal in a mini industrial direct current electrical arc furnace

Purpose The purpose of this paper is the study of the electro-vortex flow (EVF) as well as heating and melting processes for mini industrial direct current electric arc furnace (DC EAF). Design/methodology/approach A mini DC EAF was designed, manufactured and installed to study the industrial processes of heating and melting a small amount of melt, being 4.6 kg of steel in the case under study. Numerical modelling of metal melting was performed using the enthalpy and porosity approach at equal values and non-equal values of the solidus and liquidus temperatures of the metal. The EVF of the liquid phase of metal was computed using the large eddy simulation model of turbulence. Melt temperature measurements were made using an infrared camera and a probe with a thermocouple sensor. The melt speed was estimated by observing the movement of particles at the top surface of melt. Findings The thermal flux for metal heating and melting, which is supplied through an arc spot at the top surface of metal, is estimated using the thermal balance of the furnace at melting point. The melting time was estimated using numerical modelling of heating and melting of metal. The process started at room temperature and finished once whole volume of metal was molten. The evolution of the solid/melt phase boundary as well as evolution of EVF patterns of the melt was studied. Originality/value Numerical studies of heating and melting processes in metal were performed in the case of intensive liquid phase turbulent circulation due to the Lorentz force in the melt, which results from the interaction of electrical current with a self-magnetic field.

Design of Reactive Power Compensation Devices on the Base of Dynamical Simulation of Steelmaking Process

Alternative current electric arc furnaces (AC-EAF) are characterized by significant active and reactive power consumption. The operation of electric arc furnaces can cause considerable harmonic distortion and voltage fluctuations (flicker) in the electric power system due to the non-linearity of electric arc and stochastic character of arc burning. The distortion level caused by AC EAF operation depends on the stage of the steelmaking process. The novel method of design of reactive power compensation devices on the base of dynamical digital simulation of complete steelmaking process is described in the offered paper.

Study on Mechanism of Titanium Slag Smelting in DC electric Arc Furnace

Based on the production practice of a 30MWA large DC electric furnace in Yunnan, this paper discusses the basic reactions in smelting titanium slag, and discusses the thermodynamic and kinetic behaviors of the main elements and impurities. The results show that the reduction reaction in the smelting process is carried out simultaneously in the multi-component system of Al2O3-CaO-MgO-SiO2-TiO2. The titanium slag containing more than 80% TiO2 can be obtained by enrichment smelting of ilmenite in a large high-power enclosed direct current electric arc furnace, and the output includes by-product pig iron. Impurities MgO, CaO and Al2O3 in titanium concentrate will not be reduced during reduction smelting; SiO2, MnO and V2O5 will be reduced in varying degrees, and the reduced products Si, Mn and V will dissolve in the metal Fe phase or be enriched in titanium slag.

Deep Learning Based Predictive Compensation of Flicker, Voltage Dips, Harmonics and Interharmonics in Electric Arc Furnaces

In this research work, deep machine learning-based methods together with a novel data augmentation are developed for predicting flicker, voltage dip, harmonics, and interharmonics originating from highly time-varying electric arc furnace (EAF) currents and voltage. The aim with the prediction is to counteract both the response and reaction time delays of active power filters (APFs) specifically designed for electric arc furnaces (EAF). Multiple synchronous reference frame (MSRF) analysis is used to decompose the frequency components of the EAF current and voltage waveforms into dqo components. Then using low-pass filters and prediction of the future values of these dqo components, reference signals for APFs are generated. Three different methods have been developed. In two of them, a low-pass Butterworth filter is used together with a linear finite impulse response (FIR)-based prediction or long short-term memory network (LSTM) for prediction. In the third method, a deep convolutional neural network (CNN) combined with a LSTM network is used to filter and predict at the same time. For a 40-ms prediction horizon, the proposed methods provide 2.06%, 0.31%, 0.99% prediction errors of the dqo components for the Butterworth and linear prediction, Butterworth and LSTM, and CNN with LSTM, respectively. The error of the predicted reconstructed waveforms of flicker, harmonics, and interharmonics resulted in 8.5%, 1.90%, and 3.2% reconstruction errors for the abovementioned methods. Finally, a Simulink and GPU-based implementation of predictive APF using Butterworth filter + LSTM and a trivial APF resulted 96% and 60% efficiency on compensation of EAF current interharmonics.

Characteristics Analysis of Fluid Flow and Heating Rate of a Molten Bath Utilizing a Unified Model in a DC EAF

Herein, a 2D unified model coupling a plasma arc–molten bath–cavity in a direct current electric arc furnace was developed for a characteristic analysis of the fluid flow and heating rate of the molten bath. The ‘local thermodynamic equilibrium–diffusion approximation’ method was employed for the physical phenomenon at the plasma arc/molten bath interface, and the volume-of-fluid method was used to track the free surface. After ensuring model validation, the formation processes of the cavity and the flow field and heating rate of the molten bath were investigated by utilizing the unified model. The numerical results showed that the formation processes of the cavity contained three stages, namely the expansion, compression, and dynamic equilibrium stages. The arc pressure and plasma shear stress both contributed to the cavity formation, and dominated the expansion of the cavity depth and diameter, respectively. Under plasma arc jet impingement, there were two flow patterns inside the molten bath: (i) a clockwise eddy on the top surface and lateral part of molten bath dominated by plasma shear stress, and (ii) a counter-clockwise eddy in the bottom part of the molten bath dominated by the electromagnetic force. Meanwhile, the main heated region of the molten bath with the plasma arc–molten bath–cavity coupling was in the radial range of 0.2–0.6 m, and a high-temperature region was formed on the top surface of the molten bath caused by plasma shear stress.

Numerical investigation of AC electric arc plasma heat dissipation in EAF

ABSTRACT The alternating current (AC) electric arc furnace (EAF) is a crucial steelmaking facility consuming electricity to melt the recycled scrap. The arc heat dissipation impacts the scrap melting rate. The present study established a mathematical model to simulate the AC electric arc and estimate its heat dissipation under different arc operations. The study was aimed to deduce the relationship between arc operating current, arc length and the share of different arc heat dissipation mechanisms for future application in the scrap melting model. Validations of arc modelling were conducted by comparing the results against experimental data and simulations in the literature. A statistical method based on data sampling was proposed to determine the AC arc state variables to approximate the arc heat dissipation. A scrap melting experiment was designed and implemented in NLMK EAF to validate the amount of share determined in the present study for different arc heat dissipation mechanisms.

Comparison of DC Electric Arc Furnace Chaotic Models for Power Quality Indices Assessment

Due to their particular feature, direct current (dc) electric arc furnace (EAF) installations are peculiar loads that cause moderate to severe power quality (PQ) disturbances in the feeding power systems. Among them, voltage fluctuations and waveform distortions are the most impactful ones, and they should be adequately addressed in order to mitigate the detrimental effects. Several types of models have been developed in order to evaluate the effects of EAFs on networks, and chaotic models have been specifically recognized as suitable tools to evaluate the impact of EAFs in terms of PQ disturbances. This article compares the performance of three chaotic models (Chua, Lorenz, and Rössler) aiming at estimating PQ indices values of dc EAFs. The procedure exploits a block diagramming tool of the dc EAF installation and minimizes the deviation of estimated PQ indices from the actual ones through a new Monte Carlo optimization procedure, performed upon the parameters of the three chaotic models. To comply with the time-varying nature of the current and voltage waveforms in a dc EAF installation and with the wide presence of interharmonics, traditional, and advanced PQ indices are considered in this article. Actual data collected at an Italian dc EAF installation are used to conduct a numerical comparative analysis and to validate the effectiveness of the models in estimating the PQ disturbances.

Numerical Analysis of Fluid Flow and Heat Transfer by Means of a Unified Model in a Direct Current Electric Arc Furnace

Herein, a unified 2D axially symmetric model coupling the plasma arc and molten bath in a direct current electric arc furnace is established, with the physical phenomena at the plasma arc/molten bath interface to be confronted by the “local thermodynamic equilibrium diffusion approximation” method. After ensuring model validation and grid independence test, the fluid flow and heat transfer processes are analyzed. The numerical investigation reveals that in terms of the relative importance of driving forces in the molten bath, an anticlockwise eddy is formed at the molten bath center driven by electromagnetic force, whereas a clockwise eddy is formed near the lateral part of the molten bath driven by buoyancy force. Regarding the distribution of heat flux, the convection–conduction and radiation heat govern the rate of heat transfer from the plasma arc to the molten bath. In addition, for larger arc current, higher arc power in the plasma arc region and higher heating rates in the molten bath are obtained when the arc length is held constant; for shorter arc length, lower arc power in the plasma arc region but higher heat transfer efficiency in the molten bath is attained when the arc current is held constant.

Deep-Learning-Based Harmonics and Interharmonics Predetection Designed for Compensating Significantly Time-Varying EAF Currents

In this article, a new approach to compensate both the response and reaction times of active power filters (APF) for special cases of highly time-varying harmonics and interharmonics of electric arc furnace (EAF) currents is proposed. Instead of using the classical approach of taking a window of past current samples and analyzing the data, future samples of EAF currents are predetected using a deep learning (DL)-based method and then analyzed, which provides the opportunity to make real-time analysis. This can also serve the needs of other possible APF applications. Two different methods for prediction of future samples of harmonics and interharmonics have been proposed: predetection of harmonics and interharmonics in the time domain (TD) and in the frequency domain (FD). To obtain the best possible accuracy for both methods, grid search has been employed for parameter optimization of the DL structure. Both TD and FD approaches have been tested on field data, which had been obtained from transformer substations supplying EAF plants. It is shown that the response time of the APF algorithms can be compensated using the TD-based approach, while it is possible to compensate both the response and reaction times of APFs using the proposed FD-based approach. The developed method can be considered to be a feasible candidate solution for generating reference signals for the controllers of new generation of compensation devices, which can be referred to as predictive APFs.

Analysis of Power System Harmonic Subgroups of the Electric Arc Furnace Currents Based on a Hybrid Time-Frequency Analysis Method

In this paper, a hybrid time-frequency analysis method, specially developed to decompose harmonic subgroups and interharmonics of the electric arc furnace (EAF) currents, which are highly time varying due to the operation principles of the EAFs, is presented. The main objective is accurate perception of harmonics and interharmonics in cases of rapid changes or power quality (PQ) events in power system voltages and currents. Harmonic and interharmonics detection has been achieved using discrete wavelet transform (DWT), which provides time-localization in cases of highly time-varying signals. Although DWT elicits accurate spectral decomposition at low frequencies, and especially at the baseband, bandwidths of the band-pass filters increase which results in loss of accuracy at higher frequencies. In order to avoid this problem, power signals are modulated by complex exponential waveforms, which corresponds to shifting the required harmonic sub-band contents to the baseband, where the accuracy of the DWT is the best. Using the proposed hybrid combination of DWT and complex exponential modulation, time domain waveform of each harmonic sub-band of the EAF currents can be estimated close to ideal values. The method also enables to focus on any required interharmonic sub-band in addition to the harmonics. To optimize the performance of the DWT in cases of PQ events, various windowing approaches are discussed. The proposed harmonic and interharmonic estimation method has been verified on both synthetic data and EAF currents collected from the electricity transmission system. Estimated harmonic and interharmonic waveforms can serve as a good reference in many areas including active power filtering operations, limit violation determinations, etc., in the power system.

Exponential Smoothing of Multiple Reference Frame Components With GPUs for Real-Time Detection of Time-Varying Harmonics and Interharmonics of EAF Currents

In this research work, a multiple synchronous reference frame (MSRF)-based analysis method, used together with exponential smoothing (ES) to accurately obtain the time-varying harmonics and interharmonics of electric arc furnace (EAF) currents, is proposed. The proposed method has been implemented on NVIDIA Geforce GTX 960 graphics card for the parallel processing of all harmonics and interharmonics so that real-time processing of the EAF currents obtained from a measurement point of the electricity transmission system is possible. To obtain the best possible accuracy for the EAF current harmonics and interharmonics estimation, the ES order has been optimized specific to each harmonic and interharmonic frequency component. It has been shown on field data that the implemented system is capable of successfully estimating all harmonics up to the 50th and all interharmonics at 5-Hz resolution in real time. Moreover, active filtering of certain harmonics and interharmonics has been achieved in the simulation environment and it has been shown that successful real-time filtering of any harmonic and interharmonic component is possible with the proposed MSRF+ES method. Hence, it can be concluded that the proposed system can be used to generate reference signals for the controllers of the new technology compensation systems such as active power filters.

Analysis of Economic and Energy Efficiency of Using Electric-Arc Reduction for Iron-Containing Materials

For many years an important task for metallurgical scientists is the search for alternative technologies of iron production from mineral raw materials without using expensive coke, natural gas and with the least pressure on ecology. Technologies are required with maximum extraction of iron from ore, and with the least pollution of the atmosphere using cheap and least scarce reducing agents, for example power generating coal. Recently in OAO VNIIMT a new method has been developed and proven under laboratory conditions for direct preparation of iron by reduction of fine-grained material with solid reducing agent in a direct current electric arc furnace, including ground ore, iron-bearing sludge, etc. This technology makes it possible to prepare cast iron containing 3–4% carbon in a molten condition that may be granulated, cast into pigs, and treated in a ladle-furnace unit with minimum specific yield of gas and slag.

Analysis of electric arc furnaces efficiency via frequency spectrum-based arc coverage detection

We introduce the analysis of variables measured outside a direct current electric arc furnace that are related to the arc coverage and its electric energy efficiency. We derive an on-line reference to the arc coverage suitable for control decisions. In order to do so, we study the furnace shell vibration frequency spectrum as structure-borne sound sensed by a microphone. The data is analysed using real time software tools to obtain the sound signal spectrum associated with arc coverage likewise related to the optimal performance of the furnace. A statistical test is performed to prove the relationship between sound and electric energy efficiency.

Online Characterization of Interharmonics and Harmonics of AC Electric Arc Furnaces by Multiple Synchronous Reference Frame Analysis

In this paper, a multiple synchronous reference frame (MSRF) analysis framework is developed to determine the positive- and negative-sequence components of all interharmonic and harmonic currents produced by alternating current electric arc furnace (AC EAF) installations, which can be considered as balanced but asymmetrical three-phase, three-wire loads on the power system. The aim of developing the MSRF analysis framework is twofold; deep understanding of the EAF characteristics, and fast and accurate generation of reference signals to the controllers of the advanced technology compensation systems such as active power filters (APFs), synchronous static compensators (STATCOMs), and energy storage systems (ESS), which may successfully compensate interharmonics, harmonics, and flicker. Online characterization of interharmonics and harmonics by MSRF analysis utilizes continuously measured line currents and line voltages on the medium-voltage side of the EAF transformer at a sampling rate of 25.6-kHz per data channel. It has been shown by offline computations that compensation of the sequence components of interharmonics and harmonics of the EAF currents obtained by the proposed framework, reduces the short-term flicker values at the point of common coupling by up to 10-fold. The proposed MSRF analysis framework has been successfully verified by comparing the frequency spectrums of the EAF currents with Fourier analysis results based on one-cycle sliding windows of 10-cycle duration.

A comparative study of modelling AC electric arc by one-dimensional interpolation for power system harmonics analysis

Electric arc is a discharge phenomenon caused by particular electrical events and arc produced facilities in power system, for example the occurrence of short-circuit fault in feeders and the use of electric arc furnace for steel-making. All of these electric arcs have a highly nonlinear nature and can be considered as a significant source of power quality problems. To investigate the impacts of the electric arcs on power quality studies the development of the electric arc models for simulation analysis is became necessary. This paper thus presents the use of different one-dimensional interpolation methods for modelling the alternating current electric arc. The performances of different methods are compared by simulation results that are derived from the modelling of an alternating current electric arc furnace load and a low-frequency fluorescent tube.

A Neural-Network-Based Data-Driven Nonlinear Model on Time- and Frequency-Domain Voltage–Current Characterization for Power-Quality Study

An accurate model of nonlinear load is important for the evaluation of power quality (PQ). Among different PQ disturbance sources, alternating current electric arc furnace (AC EAF) is one of most complicated and serious loads. To provide effective operation prediction of AC EAF, a data-driven modeling approach based on time- and frequency-domain voltage-current (v -i) characterization is proposed in this paper. With the prediction of the proposed model in the time domain, the dynamic and short-term behavior of AC EAF can be observed. And the quasistationary and long-term features of AC EAF would be extracted via the frequency-domain phase of the proposed model. From the comparison on the field measurement data, the performance of the proposed model can be verified in the applications of PQ studies.

A Fundamental Wave Amplitude Prediction Algorithm Based on Fuzzy Neural Network for Harmonic Elimination of Electric Arc Furnace Current

Electric arc furnace (EAF) causes the harmonics to impact on the supply network greatly and harmonic elimination is a very important research work for the power quality associated with EAF. In the paper, a fundamental wave amplitude prediction algorithm based on fuzzy neural network for harmonic elimination of EAF current is proposed. The proposed algorithm uses the learning ability of the neural network to refine Takagi-Sugeno type fuzzy rules and the inputs are the average of the current measured value in different time intervals. To verify the effectiveness of the proposed algorithm, some experiments are performed to compare the proposed algorithm with the back-propagation neural networks, and the field data collected at an EAF are used in the experiments. Moreover, the measured amplitudes of fundamental waves of field data are obtained by the sliding-window-based discrete Fourier transform on the field data. The experiments results show that the proposed algorithm has higher precision. The real curves also verify that the amplitude of fundamental wave current could be predicted accurately and the harmonic elimination of EAF would be realized based on the proposed algorithm.

A Hybrid Wavelet Transform and Neural-Network-Based Approach for Modelling Dynamic Voltage-Current Characteristics of Electric Arc Furnace

This paper proposes a discrete wavelet transform (DWT) and radial basis function neural network (RBFNN)-based method for modeling the dynamic voltage-current (v- i)characteristics of the ac electric arc furnace (EAF). The objective of the study is to develop a complete model of the EAF including different operation stages, and the model can be used as a harmonics and flicker source in its connected power system for the power-quality penetration or mitigation study, where the developed model can be embedded in the power system implemented by a commonly seen simulation tool, such as Matlab/Simulink. In the study, a combination of the DWT and the sequential RBFNN with parameters initialization algorithm is proposed to build the EAF v- i characteristics with enhanced lookup tables for different operation stages, where the field measurements of the EAF voltage and current are used to train the RBFNN for modeling the EAF load. Simulation results obtained by using the proposed model are compared with different measured data. It shows that the solution procedure accurately models the EAF dynamic v- i behavior. The proposed method also can be applied to model other highly nonlinear loads to assess the effectiveness of compensation devices or to perform relative penetration studies.

Balance of Raw Input and Energy of Titanium Slag Smelting in Closed High Power Direct Current Electric Arc Furnace

There were two controlling factors, which should be kept balance of raw input and energy during titanium slag smelting in closed high power direct current electric arc furnace, DC furnace for short. The first factor contains compositions and ratio of input raw materials. Compositions of the raw could influence the progress and technical indicators of DC furnace smelting, and on the meanwhile, it could determine the compositions of impurity and contents of TiO2 in titanium slag. On the other hand, the ratio of input infected the compositions of the slag, while too much anthracite would disadvantage to produce high quality slag because of impurity being reduced continually. Meanwhile much more low-state titanium would be generated. The second factor was an important one that energy equilibrium, which influence the production safety of DC furnace. It could keep balance of input and output energy, while the heatloss was 4.971 MVA. While the ratio of anthracite remained the same, the energy would effect slag viscosity and equilibrium of freeze lining directly. The energy input higher than smelting need would result in burnthrough of furnace wall and hearth, on the contrary, lower than smelting need would bring about slagging difficulties. Consequently, raw input and energy must be kept dynamic balance in order to achieve high quality titanium slag and protect freeze lining.