Electric Arc Furnace Model Research Articles

Numerical Investigation on Heat Transfer Efficiency from Gas to Scrap in Feeding System of 100 t Consteel Electric Arc Furnace

The Consteel electric arc furnace (EAF) has gained rapid development in the field of EAF steelmaking due to its advantageous features including short tap‐to‐tap time, low production energy consumption, and waste heat recovery. However, the low heat transfer efficiency from gas to scrap has remained a persistent technical challenge in Consteel EAF. This research establishes a scrap preheating model for the Consteel EAF to comprehensively investigate the heat transfer efficiency from gas to scrap. The calculation results demonstrate that the heat transfer efficiency from gas to scrap can be effectively enhanced by increasing the initial gas velocity and scrap porosity. Specifically, raising the initial gas velocity from 0.9 to 1.5 m s−1 improves convective and radiative heat exchange, leading to increased heat transfer efficiency from gas to scrap at the same positions within the scrap layer. Moreover, increasing the scrap porosity from 0.92 to 0.98 significantly enhances the heat transfer efficiency through augmented radiation heat exchange. Notably, the improvement in heat transfer efficiency resulting from increased porosity is considerably greater than that achieved through increased gas velocity. The findings of this study hold significant practical implications for the actual field production operations in the horizontal continuous feeding system of EAF.

A New Electric Arc Furnace Model Based on Current Waveform Synthesis From Distributions of DFT Amplitudes and Power System Frequency

A New Electric Arc Furnace Model Based on Current Waveform Synthesis From Distributions of DFT Amplitudes and Power System Frequency

Application of the Deterministic Chaos in AC Electric Arc Furnace Modeling

Application of the Deterministic Chaos in AC Electric Arc Furnace Modeling

Effect of gangue on CO2 emission for different decarbonisation pathways

At present, iron and steelmaking industry worldwide is going through the transition of decarbonisation to meet its goal of reaching net zero by 2050. In addition, Australian iron and steelmaking industry is facing its own challenge of processing lower grade ores with increasing gangue content. Two major pathways are direct reduction of iron – electric arc furnace pathway (DRI-EAF) and direct reduction of iron – electric smelter-BOF (DRI-electric smelter-BOF) pathway. In the present work, a mass and energy balance model of basic oxygen furnace (BOF) and electric arc furnace (EAF) have been developed using thermodynamic software. The EAF model showed that with 100 wt-% cold DRI, the specific electric energy requirement varied between 514 and 651 kWh/tls whereas in case of hot DRI, it varied between 399 kWh/tls and 510 kWh/tls. As the gangue content increased from 10.7 wt-% to 19.1 wt-%, yield decreased from 88 wt-% to 75.5 wt-% and slag weight increased from about 200 kg/tls to 630 kg/tls. The BOF model showed that the slag produced in a BOF varied between 63 kg/tls and 73 kg/tls for lower to higher grade ores reflecting different hot metal chemistry (P, Mn) coming from different ores. The results indicated that electric smelter-BOF is more compatible to process lower grade ores than EAF where quantity of slag and loss of yield are very high with increasing gangue content. CO2 emissions from H2DRI-EAF and H2DRI-electric smelter-BOF pathway for different types of ores increased with the increasing gangue content. For H2DRI-EAF pathway, as gangue content increases from 10.7 wt-% to 19.1 wt-%, CO2 emission rises from 0.10 t/tls to 0.19 t/tls as more limestone is needed to remove the gangue which also increases the production of CO2. In case of H2DRI-electric smelter-BOF pathway, CO2 emission increases from 0.12 t/tls to 0.19 t/tls with increasing gangue content. CO2 emissions from both pathways are significantly lower than the current BF-BOF pathway.

Arc Quality Index Based on Three-Phase Cassie–Mayr Electric Arc Model of Electric Arc Furnace

In steel recycling, the optimization of Electric Arc Furnaces (EAFs) is of central importance in order to increase efficiency and reduce costs. This study focuses on the optimization of electric arcs, which make a significant contribution to the energy consumption of EAFs. A three-phase equivalent circuit integrated with the Cassie–Mayr arc model is used to capture the nonlinear and dynamic characteristics of arcs, including arc breakage and ignition process. A particle swarm optimization technique is applied to real EAF data containing current and voltage measurements to estimate the parameters of the Cassie–Mayr model. Based on the Cassie–Mayr arc parameters, a novel Arc Quality Index (AQI) is introduced in the study, which can be used to evaluate arc quality based on deviations from optimal conditions. The AQI provides a qualitative assessment of arc quality, analogous to indices such as arc coverage and arc stability. The study concludes that the AQI serves as an effective operational tool for EAF operators to optimize production and increase the efficiency and sustainability of steel production. The results underline the importance of understanding electric arc dynamics for the development of EAF technology.

New directions in electric arc furnace modeling

New directions in electric arc furnace modeling

Fractional-Order Model of Electric Arc Furnace

The paper introduces a fractional-order electric arc furnace (EAF) model. It is based on a power balance equation which, as has been shown, can be represented by a Hammerstein-Wiener model. Such a model includes a block described by a linear integer-order differential equation, in this case the order is equal to one, which can be extended to a fractional-order equation. The introduced fractional order is either constant or variable in time. The variable coefficients of the model have been identified by a genetic algorithm The proposed model has been tested using measurement data from two different EAFs and its accuracy has been compared with the classical integer-order model based on the RMS error between the model output and measurement waveforms. The results show that the fractional-order EAF model enables error reduction and consequently reflects more accurately the phenomena taking place in the furnace.

Comparison of a Dynamic Model of Electric Arc Furnace with Actual Operation Data for Voltage Flicker Analysis in Electrical Power Network

Electric arc furnaces (EAFs) used in the iron and steel manufacturing industry for melting and refining scrap metals are one of the most disturbing loads that exhibit unbalanced and highly nonlinear characteristics. Serious voltage fluctuations occur in the power system as a result of the rapid change in the current drawn from the grid by the EAF. Voltage fluctuations lead to a power quality problem known as flicker, which is defined as observable changes in light sources that affect the production environment, cause eye fatigue in personnel, and lower the work concentration levels. To investigate the voltage flicker problem, an accurate mathematical model describing the behavior of the EAF load is required. In this study, a dynamic EAF model that can be adjusted to different operating conditions has been developed in the time domain. The electric arc voltage has been modeled as an externally controllable voltage source. The instantaneous arc voltage has been expressed as a function of the arc length independent of the current. The arc resistance, which varies with time and is nonlinear, has also been calculated with differential equations using the instantaneous arc voltage value. To measure the short-term flicker severity index caused by the EAF in the power system, a flicker meter in compliance with the International Electrotechnical Commission (IEC) 61000-4-15 standard has been designed. The current-voltage characteristics of the EAF, its effect on the power system, and the flicker severity occurring at the point of common coupling (PCC) have been analyzed with simulation studies using the PSCAD/EMTDC software. Besides, the simulation results of the dynamic model of the EAF have been compared with the results obtained from the model based on the measured field data.

A meta-heuristic optimization-based method for parameter estimation of an electric arc furnace model

Throughout distribution systems, it is usual to find non-linear time-varying loads, such as electric arc furnaces (EAFs), which are widely used in the steel-making industrial sector. Due to the process of melting and refining metals, the EAFs consume large blocks of power (active and reactive power) causing significant power quality disturbances, such as harmonics and voltage fluctuations on distribution networks. Different EAF parametric models have been proposed with the purpose to predict the voltage and current waveforms and then evaluate the performance of the reactive power compensation devices. This paper proposes a novel methodology for the optimal estimation of parameters of an electric arc furnace model, which can achieve lower execution times and error rates compared to some state-of-the-art methods. The methodology was evaluated using three meta-heuristic optimization algorithms such as the particle swarm optimization (PSO) algorithm, the vortex search algorithm (VSA) and the crow search algorithm (CSA); using real and simulated data. From the results, the proposed methodology based on meta-heuristic optimization approaches worked efficiently, allowed estimating the parameters of the electric arc furnace model using a single optimization step, capture the non-sinusoidal, non-linearity and time-varying random behavior that exhibit the real electric arc furnace samples and obtained relative errors of the total harmonic distortion between the measured and estimated voltage and arc current signals around 1.23% and 0.62%, respectively.

Estimation and Analysis of the Electric Arc Furnace Model Coefficients

This paper is devoted to electric arc furnace (EAF) modeling using a random differential equation based on the power balance equation. The proposed approach broadens and improves the model through the introduction of stochastic processes in place of existing coefficients. The paper presents a method which enables the estimation of EAF model coefficients with the help of measurement data - voltage and current waveforms recorded during the melting stage of an EAF work cycle. The estimation process is conducted with a Monte Carlo method and genetic algorithm, which is applied iteratively to each of the defined frames of the input signal. The estimated coefficients have been analyzed with respect to their time variability as well as the probability distributions of their values and increments. The results have been extensively visualized. Next, the identification of the stochastic processes representing the model coefficients has been carried out. Based on the previous results and autocorrelation functions, the density functions and parameters of discrete-time stochastic processes were identified. The paper presents solutions validated with statistical tests.

Modeling and Energy Efficiency Analysis of the Steelmaking Process in an Electric Arc Furnace

This paper presents a comprehensive model of an industrial electric arc furnace (EAF) that is based upon several rigorous first-principles submodels of the heat exchange in the EAF and practical experience from an industrial melt shop. The model is suited for process simulation, optimization, and control applications. It assumes that the energy demand of the process is satisfied by six sources, the electric arc, the oxy-fuel burners, the oxygen lances, the combustion of coal, and the oxidation of metal in the liquid and in the solid phase. The energy exchange between the liquid and the solid phase due to liquid metal splashing is also considered. The different mechanisms of heat exchange are represented in the model as follows: (a) the radiative heat exchange from the arc to the other phases is computed using the DC circuit analogy, where the view factors are calculated using exact formulae and Monte-Carlo algorithms. (b) The energy input from the oxy-fuel burner is modeled using simplified geometries for which heat transfer relationships are known. (c) The amount of heat released by the oxidation of solid metal is described by the quadratic corrosion formula. (d) The energy exchange from the bath to the solid phase due to splashing is modeled using relationships and experimental data that are available in the literature. The model contains the melting rates and the efficiency of the oxygen lancing as free parameters; their values were computed by a least squares fit to process data of an industrial Ultra-High-Power EAF. In comparison with existing EAF models, the model presented here describes the dynamic behavior of the melting process more realistically. Based on the model, time-dependent energy efficiency curves for the various contributions and for the overall process are computed and discussed.

Application of Shallow Neural Networks in Electric Arc Furnace Modeling

Electric arc furnaces (EAFs) are important appliances in the steelmaking industry, but they are characterized by a nonlinear, dynamic, and stochastic nature. Due to this fact, EAFs can have a negative influence on power systems. Measures to mitigate such problems can be designed properly only with knowledge of the influence of the load on the system. Therefore, it is necessary to have accurate EAF models that reflect the complicated character of such loads. Researchers use different approaches for EAF modeling, such as stochastic process analysis, differential equation models, or neural networks. This article presents the application of three models based on artificial neural networks (ANNs) in EAF modeling along with an extension based on the stochastic moving average (MA) process. The goal was to provide ANN models that are simple in structure in comparison to, e.g., deep learning methods used by other researchers. The first two models are built on multilayer perceptron networks, and the third applies a nonlinear autoregressive exogenous model with the help of differential equation transformed into the Hammerstein–Wiener model. ANN models are improved by adding an MA ingredient. The article describes the measurement data, the design of each approach, and the results of the EAF modeling.

Optimal Electric Arc Furnace Model’s Characteristics Using Genetic Algorithm and Particle Swarm Optimization and Comparison of Various Optimal Characteristics in DIgSILENT and EMTP-RV

The stochastic and nonlinear characteristics of electric arc furnaces (EAFs) lead to power quality challenges in the power system. In studying EAF behaviors, having optimized characteristics/models, selecting a suitable and optimum model that adapts to the actual characteristics of EAFs, and investigating simulation software’s capability for implementing EAF models are essential. However, the literature shows a research gap in investigating EAF simulations in various software products based on different models. This paper studies several time-domain models, such as piece-wise linear, modified piece-wise linear, hyperbolic, exponential, and exponential-hyperbolic models, for EAF modeling and simulation. The optimal estimation of parameters for the introduced models is necessary to adapt actual EAF characteristics. Thus, one of the studies taken in this paper is optimizing the EAF model’s characteristics. The proposed optimization problem is solved using the genetic algorithm (GA) and particle swarm optimization (PSO). Moreover, the optimized models are simulated in DIgSILENT and EMTP-RV to investigate different EAF models from the viewpoint of accuracy and efficiency. The optimization of different EAF models’ characteristics and comparison of EMTP-RV and DIgSILENT in simulating EAF behavior are the contributions of this paper. The proposed method is validated based on the actual data of a realistic EAF-based steel company in Iran. The obtained results show that the modified piece-wise linear model has the most accuracy in identifying the EAF behavior. The test results based on DIgSILENT and EMTP-RV simulations imply that the EAF could be simulated with high accuracy using modified piece-wise linear and piece-wise linear models. In general, EMTP-RV has expressed more accuracy in simulating different EAF models, and the simulation execution speed of EMTP-RV is around 2.5 times faster than DIgSILENT. In contrast, DIgSILENT is more suitable to facilitate the power system studies of EAF according to its extensive study tools and library.

A Support Vector machine-Based method for parameter estimation of an electric arc furnace model

In the iron and steel industry, electric arc furnaces (EAFs) are used in the melting and refining process of metals. They are known to demand large amounts of reactive power and cause significant power quality (PQ) problems due to their highly non-linear time varying voltage-current characteristic. Several EAF models have been proposed with the purpose to predict the voltage and current waveforms, to assess the performance of different compensating devices such as static var compensator, synchronous static compensator, active power filters, and –still under study– energy storage systems, and also for planning the installation of iron and steel facilities considering existing real data from similar facilities. An important aspect of these models is related to the estimation of their parameters. This paper presents a new method to estimate the parameters of an EAF model. The method utilizes a multiple-input multiple-output regressor based on support vector machine, that maps from voltage characteristics of the electric arc to the values of the model parameters. The multidimensional support vector regressor (M-SVR) is designed in the training phase, using data from several simulations of the EAF model. These simulations are carried out adjusting the parameters of the model within the search space, and considering the real arc current as input to the model. Then, in the validation phase, for the real voltage waveform, the estimated parameters are obtained using each regressor of the M-SVR. The proposed method is validated by the comparison between the waveforms obtained using the EAF model with actual data from a steel plant. Results show that the relative error between the fundamental component of the current and voltage, for real and simulated waveforms, are 2.1% and 6.3% respectively.

Xây dựng và khảo sát sự thay đổi của các thông số lò hồ quang điện dựa trên mô hình bảo toàn năng lượng bằng phần mềm Pscad

Actual analysis showed that the arc furnace current contains many harmonics that adversely affect the power quality. There are many domestic and foreign reports on modeling and assessing the impact of EAF on the grid based on different models. However, EAF's selection of capacity for research and application of power quality improvement devices suitable to the power level has not been mentioned, these models are mainly built on Matlab Simulink software. should be primarily academic. PSCAD is one of the widely used software for electrical system simulation and is used by large companies such as ABB, Korean power corporation Kepco. Building EAF model with PSCAD software will increase the ability to apply simulation results into practice. The objective of the paper is to build an electric arc furnace model based on the energy conservation model with PSCAD software, thereby assessing the change of parameters in the model and the effect of this load on electricity grid during operation.

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.

Measurement-Based Electric Arc Furnace Model Using Ellipse Formula

This paper proposes a mathematical model of an electric arc furnace (EAF) system based on the voltage and current (V–I) field data. The proposed method is formulated using the ellipse equation, which is derived from the measurement profile of real EAF systems. This paper also presents the harmonics and power-pattern information corresponding to the smelting processes (boring, melting, and refining) in order to realistically design an EAF for potential industrial applications. The third harmonic component significantly affects the shape of the V–I curve in realistic EAF systems, while the periodic power fluctuation is closely associated with the size of the ellipse. These characteristics can be formulated and designed by the proposed method, and even in the absence of the V–I profile, an EAF model can be generated using the generalized information provided in this paper. The electrical phenomenon in the proposed method is mathematically verified using the PSCAD/EMTDC software.

Fibers of Failure: Classifying Errors in Predictive Processes

Predictive models are used in many different fields of science and engineering and are always prone to make faulty predictions. These faulty predictions can be more or less malignant depending on the model application. We describe fibers of failure (FiFa), a method to classify failure modes of predictive processes. Our method uses Mapper, an algorithm from topological data analysis (TDA), to build a graphical model of input data stratified by prediction errors. We demonstrate two ways to use the failure mode groupings: either to produce a correction layer that adjusts predictions by similarity to the failure modes; or to inspect members of the failure modes to illustrate and investigate what characterizes each failure mode. We demonstrate FiFa on two scenarios: a convolutional neural network (CNN) predicting MNIST images with added noise, and an artificial neural network (ANN) predicting the electrical energy consumption of an electric arc furnace (EAF). The correction layer on the CNN model improved its prediction accuracy significantly while the inspection of failure modes for the EAF model provided guiding insights into the domain-specific reasons behind several high-error regions.

Phase-Locked Strategy of Photovoltaic Connected to Distribution Network With High Proportion Electric Arc Furnace

With the rapid development of industrialization, the proportion of electric arc furnaces (EAF) in distribution networks is getting higher and higher. Aiming at the problem that grid voltage is distorted by a large number of arc furnace nonlinear loads accessing to distribution networks, it is crucial to make dynamic analysis of distribution network voltage and adopt a control strategy of grid-connected converter based on the new phase-locked loop (PLL) technology. As a result, the harmonic distortion rate is reduced and the quality of grid-connected current and voltage is improved. In this paper, photovoltaic (PV) system model and the typical dynamic model of the EAF are established. By analyzing the influence of the EAF model on the PV grid-connected converter with the traditional phase-locked loop while connected to distribution network, a control strategy of the PV grid-connected converter with the self-adjusting double SOGI (MAF-SASOGI) phase-locked loop with the ideal low-pass filter is proposed. Through the simulation analysis, it is verified that precise phase locked can be realized and harmonic content of the system can be reduced by the PLL strategy proposed in this paper. Therefore, the quality of PV grid-connected voltage and current is improved.

Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace

The electric arc furnace (EAF) contributes to almost one-third of the global iron and steel industry, and its harmonic pollution has drawn attention. An accurate EAF harmonic model is essential to evaluate the harmonic pollution of EAF. In this paper, a data-driven compartmental modeling method (DCMM) is proposed for the multi-mode EAF harmonic model. The proposed DCMM considers the coupling relationship among different frequencies of harmonics to enhance the modeling accuracy, meanwhile, the dimensions of the harmonic dataset are reduced to improve computational efficiency. Furthermore, the proposed DCMM is applicable to establish a multi-mode EAF harmonic model by dividing the multi-mode EAF harmonic dataset into several clusters corresponding to the different modes of the EAF smelting process. The performance evaluation results show that the proposed DCMM is adaptive in terms of establishing the multi-mode model, even if the data volumes, number of clusters, and sample distribution change significantly. Finally, a case study of EAF harmonic data is conducted to establish a multi-mode EAF harmonic model, showing that the proposed DCMM is effective and accurate in EAF modeling.