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Improvement of electrical arc furnace operation with an appropriate model

Author

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  • Hocine, Labar
  • Yacine, Djeghader
  • Kamel, Bounaya
  • Samira, Kelaiaia Mounia

Abstract

Electrical arc furnaces are commonly employed in industry to produce molten steel by melting iron and scrap steel. Furnace control is a necessary operation for production optimization. The principal parameters to be controlled are: maximum productivity requirements, minimum power off time, good power quality and safety. The aim of this study is to achieve all these objectives. Hence, because of the stochastic and dynamic behaviour of the arc during the melting process, a proposed model is checked with measurements at an industrial electrical arc furnace. How electrodes position and transformer taps can affect X and R arc function are discussed in detail. This new operating strategy has been determined taking into account Flicker, melting stages and electrode positions. It is shown that optimum efficiency can be reached by the integration of the proposed model in regulation loop.

Suggested Citation

  • Hocine, Labar & Yacine, Djeghader & Kamel, Bounaya & Samira, Kelaiaia Mounia, 2009. "Improvement of electrical arc furnace operation with an appropriate model," Energy, Elsevier, vol. 34(9), pages 1207-1214.
    • Handle: RePEc:eee:energy:v:34:y:2009:i:9:p:1207-1214
    DOI: 10.1016/j.energy.2009.03.003
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    References listed on IDEAS

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    Cited by:

    1. Bernard Baron & Tomasz Kraszewski & Dariusz Kusiak & Tomasz Szczegielniak & Zygmunt Piątek, 2023. "The Synthesis of a Bifilar Short Electric Network for a Submerged Arc Furnace with Delta-Connected Electrodes," Energies, MDPI, vol. 16(21), pages 1-21, October.
    2. Miha Kovačič & Klemen Stopar & Robert Vertnik & Božidar Šarler, 2019. "Comprehensive Electric Arc Furnace Electric Energy Consumption Modeling: A Pilot Study," Energies, MDPI, vol. 12(11), pages 1-13, June.
    3. Chen, Zhengjie & Ma, Wenhui & Wu, Jijun & Wei, Kuixian & Yang, Xi & Lv, Guoqiang & Xie, Keqiang & Yu, Jie, 2016. "Influence of carbothermic reduction on submerged arc furnace energy efficiency during silicon production," Energy, Elsevier, vol. 116(P1), pages 687-693.
    4. Gajic, Dragoljub & Savic-Gajic, Ivana & Savic, Ivan & Georgieva, Olga & Di Gennaro, Stefano, 2016. "Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks," Energy, Elsevier, vol. 108(C), pages 132-139.
    5. 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.
    6. 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).
    7. 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.
    8. Yazdani-Asrami, Mohammad & Mirzaie, Mohammad & Shayegani Akmal, Amir Abbas, 2013. "No-load loss calculation of distribution transformers supplied by nonsinusoidal voltage using three-dimensional finite element analysis," Energy, Elsevier, vol. 50(C), pages 205-219.

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