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Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks

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  • Gajic, Dragoljub
  • Savic-Gajic, Ivana
  • Savic, Ivan
  • Georgieva, Olga
  • Di Gennaro, Stefano

Abstract

The objective of this research was to use state-of-the-art artificial neural network approach to estimate the extent and effect of fluctuations in the chemical composition of stainless steel at tapping of an electric arc furnace, and thus scrap and alloy weights in the charge material mix, on the specific electrical energy consumption. Such an estimation would help to further evaluate process control strategies and optimize overall operation of the electric arc furnace. The multilayer perceptron architecture 5-5-1 with hyperbolic tangent function in the hidden layer and linear function in the output layer was used as an optimal neural network model. The model was built, tested and validated based on experimental melts of the electric arc furnace at a melt shop in Italy. The proposed model was presented as an adequate one based on the coefficient of determination (R2) which was above 0.9 as well as other error parameters calculated. The highest effect on the electrical energy consumption has carbon content.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:108:y:2016:i:c:p:132-139
    DOI: 10.1016/j.energy.2015.07.068
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. Senhui Wang & Haifeng Li & Yongjie Zhang & Zongshu Zou, 2019. "An Integrated Methodology for Rule Extraction from ELM-Based Vacuum Tank Degasser Multiclassifier for Decision-Making," Energies, MDPI, vol. 12(18), pages 1-15, September.
    4. Grzegorz Komarzyniec, 2022. "Cooperation of an Electric Arc Device with a Power Supply System Equipped with a Superconducting Element," Energies, MDPI, vol. 15(7), pages 1-18, March.
    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. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
    7. 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.
    8. 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).
    9. Zbigniew Olczykowski, 2022. "Arc Furnace Power-Susceptibility Coefficients," Energies, MDPI, vol. 15(15), pages 1-21, July.

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