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Energies, Vol. 10, Pages 1424: Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient

Energies, Vol. 10, Pages 1424: Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient

Energies doi: 10.3390/en10091424

Authors: Raul Garcia-Segura Javier Vázquez Castillo Fernando Martell-Chavez Omar Longoria-Gandara Jaime Ortegón Aguilar

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.

Authors:   Garcia-Segura, Raul ; Vázquez Castillo, Javier ; Martell-Chavez, Fernando ; Longoria-Gandara, Omar ; Ortegón Aguilar, Jaime
Journal:   Energies
Volume:   10
edition:   9
Year:   2017
Pages:   1424
DOI:   10.3390/en10091424
Publication date:   16-Sep-2017
Facts, background information, dossiers
  • steel
  • iron
  • energy efficiency
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