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A. Sadeghiana and J. D. Lavers, “Dynamic Reconstruction of Nonlinear v–i Characteristic in Electricarc Furnaces Using Adaptive Neuro-Fuzzy Rule-Based Networks,” Applied Soft Computing, Vol. 11, No. 1, January 2011, pp. 1448-1456. doi:10.1016/j.asoc.2010.04.016

has been cited by the following article:

• TITLE: Modelling and Artificial Intelligence-Based Control of Electrode System for an Electric Arc Furnace

  AUTHORS: Mahmood Moghadasian, Emad Alenasser

  KEYWORDS: Electric Arc Furnace, Electrode, PI Controller, Fuzzy Logic, Genetic Algorithm

  JOURNAL NAME: Journal of Electromagnetic Analysis and Applications, Vol.3 No.2, March 4, 2011

  ABSTRACT: This paper presents a new application of a genetic-fuzzy control system which controls the input energy to a three phase electric arc furnace. Graphite electrodes are used to convert electrical energy into heat via phase electric arcs. Con-stant arc length is desirable as it implies steady energy transfer from the graphite electrodes to the metallic charge in the furnace bath. With the charge level constantly changing, the electrodes must be able to adjust for the arc length to remain constant. A fuzzy PI controller tuned with genetic algorithms has been developed to be responsible for the ver-tical adjustment of the electrode tip displacement according to specified set-points to ensure that the arc lengths remain as constant as possible. The simulation results show that the system performances are satisfactory using the proposed method.