Dynamic reconstruction of nonlinear characteristic in electric arc furnaces using adaptive neuro-fuzzy rule-based networks

Sadeghian, Alireza; Lavers, J.D.

doi:10.1016/j.asoc.2010.04.016

Cited by 34 publications

(31 citation statements)

References 18 publications

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“…As far as it was proposed in [25], a self-tuning control can be applied, by introducing a measurement of current and voltage at the secondary, i.e., by monitoring the electric impedance of the arc and imposing a suitable displacement of the electrode to change the arc length and reach the reference impedance. The control law can be based on some approaches, including the optimal [8], neuro-fuzzy [27] and nonlinear feedback controls [28]. As already discussed in the literature [7], [8], a model to predict the position of the electrode tip may enhance quite a lot the regulation of the control system.…”

Section: Position Control Systemmentioning

confidence: 99%

Design and Structural Optimization of the Electric Arc Furnace Through a Mechatronic-Integrated Modeling Activity

Brusa

Morsut

2015

IEEE/ASME Trans. Mechatron.

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Vibration of electrodes in operation strongly affects the efficiency of the electric arc furnace (EAF) fed by ac current during the steel smelting process. Therefore, an effective control of the structural dynamics through an active system is a current goal of the "intelligent manufacturing" approach. A vertical position control applied to each electrode allows keeping the arc length almost constant and reduces the effects of some electromechanical actions due to the mutual magnetic induction among the three electric phases. Nevertheless, control action needs for a detailed model of the whole system dynamic behavior. A new method for modeling the equipment behavior and somehow the process was implemented. A key issue was including into the model all the electromechanical coupling effects occurring in this system and suitably linking to the structural dynamics. Modeling activity was performed by resorting to the multibody dynamics and the finiteelement method, while some analytical formulations were used to describe both the electric arc behavior and the control. A preliminary validation on a real plant was performed as far as the huge size of the system allowed and an assessment of the mechanical design of the EAF was completed.

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Section: Position Control Systemmentioning

confidence: 99%

Design and Structural Optimization of the Electric Arc Furnace Through a Mechatronic-Integrated Modeling Activity

Brusa

Morsut

2015

IEEE/ASME Trans. Mechatron.

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“…Such a model is then evaluated (used) by considering its generalization capability on unseen test patterns. Pattern recognition techniques can be grouped in two mainstream approaches: discriminative, such as support vector machines (Schölkopf and Smola 2002) and adaptive fuzzy inference systems (Sadeghian and Lavers 2011), and generative, like the hidden Markov models (Bicego et al 2004) and the recently-developed deep convolutional neural networks (Sainath et al 2014). Of course, also hybridized formulations exist.…”

Section: Computational Intelligence Methodsmentioning

confidence: 99%

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

Livi

Sadeghian

2015

Granul. Comput.

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Data granulation emerged as an important paradigm in modeling and computing with uncertainty, exploiting information granules as the main mathematical constructs involved in the context of granular computing. In this paper, we comment on the importance of data granulation in computational intelligence methods. Toward this aim, we discuss also the peculiar aspects related to the analysis of non-geometric patterns, which have recently attracted considerable attention by researchers. As a conclusion, we elaborate over the fundamental, conceptual problems underlying the process of data granulation, which drive the quest for a sound theory of granular computing.

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“…The current traversing the metallic charge raises its temperature until the fusion point is reached, initiating the melting process. The V-I characteristic of the EAF is the result of a nonlinear process with memory [31], since the electrical current value is determined as a function of both the voltage and the internal state, which in turn is determined by the past states of the system. Such a process gives rise to an electrical phenomenon called hysteresis [6].…”

Section: Introductionmentioning

confidence: 99%

On the Long-Term Correlations and Multifractal Properties of Electric Arc Furnace Time Series

Livi

Maiorino

Rizzi

et al. 2016

Int. J. Bifurcation Chaos

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In this paper, we study long-term correlations and multifractal properties elaborated from time series of three-phase current signals from an industrial electric arc furnace. Implicit sinusoidal trends are suitably detected by considering the scaling of the fluctuation functions. Time series are then filtered via a Fourier-based analysis to remove such strong periodicities. In the filtered time series we detected long-term, positive correlations. The presence of positive correlations is in agreement with the typical V–I characteristic (hysteresis) of the electric arc furnace, thus providing a sound physical justification for the memory effects found in the current time series. The multifractal signature is strong enough in the filtered time series to be effectively classified as multifractal

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Dynamic reconstruction of nonlinear characteristic in electric arc furnaces using adaptive neuro-fuzzy rule-based networks

Cited by 34 publications

References 18 publications

Design and Structural Optimization of the Electric Arc Furnace Through a Mechatronic-Integrated Modeling Activity

Design and Structural Optimization of the Electric Arc Furnace Through a Mechatronic-Integrated Modeling Activity

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

On the Long-Term Correlations and Multifractal Properties of Electric Arc Furnace Time Series

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