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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. [ABSTRACT FROM AUTHOR]

A closed-form solution for power balance equation of electric arc furnace (EAF) is proposed in this paper based on field measurements of the instantaneous furnace voltage and current. The method is developed based on an analytical solution of power balance equation in general case. The closed-form solution is dependent on the EAF parameters, which should be found by parameter identification procedure carrying out by minimization of an appropriate objective function. The minimization is performed by means of a calculus-based optimization method, namely, interior-point method. The validity of the proposed method is verified by comparing its results with those of the field measurements. The results show quite satisfactory correlation between the proposed method and the actual EAF operation. [ABSTRACT FROM AUTHOR]

SUMMARY: Being able to predict future temperatures on the wall lining is key when controlling and scheduling maintenance for large industrial smelting furnaces. In this paper, we propose and test a machine learning approach for predicting lining temperatures in a ferronickel smelting furnace. This approach was deployed and evaluated in a real‐world scenario, i.e., in one of Cerro Matoso S.A.'s (CMSA) industrial plant furnaces. Different techniques were tested, and finally, a multitarget regression (MTR) model showed the best performance. Previous state of the art focused on predicting only one target sensor; in contrast, our model is capable of predicting up to 12 targets. Two MTR models were tested: the incremental structured output prediction tree (iSOUP‐Tree) and the stacked single‐target Hoeffding tree regressor (SST‐HT). The SST‐HT method had the best behavior in terms of the average mean absolute error (AMAE) and average root mean square error (ARMSE). The results indicate that the developed MTR models can accurately predict the measured temperature on multiple point sensors. Results of this work are expected to help the process of structural control and health monitoring of the furnace linings located at CMSA's plant. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to the modelling of electric arc furnaces based on representative samples of voltages and currents. These samples represent characteristic 1-s intervals and are extracted from the measured signals of longer operating periods of the arc furnace. Each of these samples contains a unique combination of interharmonic voltages and currents that represent the fingerprint of the observed furnace. On this basis, a method for modelling the dynamic variation of the electric arc is proposed, which can be used to model the electric arc furnace's operation. A general model of the furnace is thus obtained, which can be easily adapted for each installed furnace in a system. The designed model is based on the u-i characteristic of the furnace load, that is, the reciprocal correlation between the voltage and the current of the electrical arc is obtained. The proposed model can be used for power-quality studies, for studies of the flicker propagation and mitigation, and also for the computation and design of new compensation devices. Simulations results are compared with actual measurements, showing good agreement between the two. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the electric arc furnace (EAF) steelmaking process, electric arc furnace dust (EAFD) is inevitably generated. The dust injection method is known as one of the effective technologies to reduce the total amount of EAFD generation. In this technology, Zn in the injected dust is enriched in the secondary dust, and Fe is transferred to the slag. In this work, the effect of dust injection technology on the recovered/lost amounts of Zn and Fe is quantitatively evaluated based on the mass balance calculation. Furthermore, in terms of dust and slag treatment costs, the potential of new EAFD treatment technology (the lime addition method) which enables simultaneous Zn and Fe recovery is discussed. By introducing the lime addition method, Zn and Fe can be recovered as resources, and the total income from these sales outweighs the cost of the chemicals used for recovery. This indicates that the treatment costs paid by EAF companies can be significantly low. The dust injection technology reduces the total quantity of dust shipped, but increases the total amount of slag and Fe loss. Therefore, it is necessary to determine the appropriate value of the amount of dust injection by balancing the waste treatment cost and the amount of metal resource recovery. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a novel feeding system based on matrix converter power supply for DC electric arc furnaces. This recommended topology possesses many promising features like overcoming the disadvantages of conventional DC power supply systems, improving the efficiency of feeding system, and power quality indices improvement in the grid side. It comprises a matrix converter controlled by space vector modulation (SVM) strategy with a single‐sided switching pattern and a six‐pulse diode rectifier. The primary function of this topology is to regulate the rms value of fixed input voltage of AC source to the proper value utilizing matrix converter, and then a six‐pulse diode rectifier can be applied to provide the required DC voltage for supplying DC arc furnace. The selection of matrix converter has been made because of particular advantages such as controllable input power factor, mitigation of input current harmonics, and controllable output frequency. The suggested configuration predominates because of the lower number of rectifier units and smoothing reactors. Moreover, the suggested feeding system does not have any current or power‐sharing problems, and only one proportional‐integral (PI) controller is adequate to control the DC arc current or its power. Note that the matrix converter used in this topology is three‐phase to three‐phase and utilized an improved passive filter (IPF) at the input side. [ABSTRACT FROM AUTHOR]

Summary: Power demand control is required at steelmaking plants and other energy intensive industrial plants where the electrical bills include high demand charges and are subject to demand limit contracts. This paper introduces a novel and simplified algorithm for the implementation of a power demand controller. The presented algorithm does not require a model of the electrical load since it is based on a geometrical approach for the estimation of the disconnection time of the controllable load and only requires the nominal power values and the load factors of both the total (full) load and the noncontrollable (base) load as parameters. The power demand control method presented is verified with a computational simulation, and precise control of the demand limit is achieved under random variations of the base and full loads. The proposed control algorithm is simpler than commercial methods and achieves a competitive performance. Simulation results show a better performance by calculating the disconnection times based on load parameters specified as mean values between the average and nominal powers of the full and base loads. Some considerations to tune‐up the load factor parameters are also discussed. The presented control method has been successfully implemented on industrial controllers in two steelmaking facilities in Mexico, having an electric arc furnace as controllable load in both cases. This novel demand control is adequate for industrial plants with electrical loads that can be disconnected without affecting the quality of the product and with minor impact on productivity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: Delays in reactive power measurement and thyristor ignition limit SVC performance in flicker mitigation of electric arc furnaces (EAFs). To overcome this limitation, prediction methods can be employed to forecast the EAF reactive power for half cycle ahead, used as a reference signal of the SVC. The utilized prediction methods in this area can be divided into linear and black‐box approaches. However, the linear approaches cannot extract the nonlinear governed relations, and using a black‐box model is not efficient for linear relations. A Gray‐ANN method is proposed here to take the advantages of the two mentioned approaches. Results from indices based on actual records of Mobarakeh Steel Company confirm superiorities of the proposed method over previously utilized prediction methods in this application. Furthermore, SVC's flicker mitigation ability is evaluated using the actual data. The results confirm the significant reduction of flicker compared with the regular system. [ABSTRACT FROM AUTHOR]