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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Raul Garcia-Segura & Javier Vázquez Castillo & Fernando Martell-Chavez & Omar Longoria-Gandara & Jaime Ortegón Aguilar, 2017. "Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient," Energies, MDPI, vol. 10(9), pages 1-11, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1424-:d:112201
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Zbigniew Łukasik & Zbigniew Olczykowski, 2020. "Estimating the Impact of Arc Furnaces on the Quality of Power in Supply Systems," Energies, MDPI, vol. 13(6), pages 1-30, March.
    2. Haobo Xu & Zhenguo Shao & Feixiong Chen, 2019. "Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace," Energies, MDPI, vol. 12(22), pages 1-15, November.
    3. Manojlović, Vaso & Kamberović, Željko & Korać, Marija & Dotlić, Milan, 2022. "Machine learning analysis of electric arc furnace process for the evaluation of energy efficiency parameters," Applied Energy, Elsevier, vol. 307(C).
    4. Andriy Lozynskyy & Jacek Kozyra & Zbigniew Łukasik & Aldona Kuśmińska-Fijałkowska & Andriy Kutsyk & Yaroslav Paranchuk & Lidiia Kasha, 2022. "A Mathematical Model of Electrical Arc Furnaces for Analysis of Electrical Mode Parameters and Synthesis of Controlling Influences," Energies, MDPI, vol. 15(5), pages 1-19, February.
    5. Jacek Kozyra & Andriy Lozynskyy & Zbigniew Łukasik & Aldona Kuśmińska-Fijałkowska & Andriy Kutsyk & Grzegorz Podskarbi & Yaroslav Paranchuk & Lidiia Kasha, 2022. "Combined Control System for the Coordinates of the Electric Mode in the Electrotechnological Complex “Arc Steel Furnace-Power-Supply Network”," Energies, MDPI, vol. 15(14), pages 1-21, July.
    6. Zbigniew Olczykowski, 2022. "Arc Voltage Distortion as a Source of Higher Harmonics Generated by Electric Arc Furnaces," Energies, MDPI, vol. 15(10), pages 1-23, May.

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