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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]

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]

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]