Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient

Raul Garcia-Segura, Javier Vázquez Castillo, Fernando Martell-Chavez, Omar Longoria-Gandara and Jaime Ortegón Aguilar
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Raul Garcia-Segura: Deparment of Engineering, University of Quintana Roo, Chetumal 77019, Mexico
Javier Vázquez Castillo: Deparment of Engineering, University of Quintana Roo, Chetumal 77019, Mexico
Fernando Martell-Chavez: Research Center in Optics, Aguascalientes 20200, Mexico
Omar Longoria-Gandara: Department of Electronics, Systems and IT, ITESO, Tlaquepaque 45604, Mexico
Jaime Ortegón Aguilar: Deparment of Engineering, University of Quintana Roo, Chetumal 77019, Mexico

Energies, 2017, vol. 10, issue 9, 1-11

Abstract: Electric arc furnaces (EAFs) contribute to almost one third of the global steel production. Arc furnaces use a large amount of electrical energy to process scrap or reduced iron and are relevant to study because small improvements in their efficiency account for significant energy savings. Optimal controllers need to be designed and proposed to enhance both process performance and energy consumption. Due to the random and chaotic nature of the electric arcs, neural networks and other soft computing techniques have been used for modeling EAFs. This study proposes a methodology for modeling EAFs that considers the time varying arc length as a relevant input parameter to the arc furnace model. Based on actual voltages and current measurements taken from an arc furnace, it was possible to estimate an arc length suitable for modeling the arc furnace using neural networks. The obtained results show that the model reproduces not only the stable arc conditions but also the unstable arc conditions, which are difficult to identify in a real heat process. The presented model can be applied for the development and testing of control systems to improve furnace energy efficiency and productivity.

Keywords: arc length modeling; artificial neural networks (ANN); electric arc furnace; EAF simulation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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