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Comprehensive Electric Arc Furnace Electric Energy Consumption Modeling: A Pilot Study

Author

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  • Miha Kovačič

    (Štore Steel Ltd., Železarska cesta 3, SI-3220 Štore, Slovenia
    Faculty of Mechanical Engineering, University in Ljubljana, Aškerčeva 6, SI-1000 Ljubljana, Slovenia)

  • Klemen Stopar

    (Štore Steel Ltd., Železarska cesta 3, SI-3220 Štore, Slovenia)

  • Robert Vertnik

    (Štore Steel Ltd., Železarska cesta 3, SI-3220 Štore, Slovenia
    Faculty of Mechanical Engineering, University in Ljubljana, Aškerčeva 6, SI-1000 Ljubljana, Slovenia)

  • Božidar Šarler

    (Faculty of Mechanical Engineering, University in Ljubljana, Aškerčeva 6, SI-1000 Ljubljana, Slovenia
    Institute of Metals and Technology, Lepi pot 11, SI-1000 Ljubljana, Slovenia)

Abstract

The electric arc furnace operation at the Štore Steel company, one of the largest flat spring steel producers in Europe, consists of charging, melting, refining the chemical composition, adjusting the temperature, and tapping. Knowledge of the consumed energy within the individual electric arc operation steps is essential. The electric energy consumption during melting and refining was analyzed including the maintenance and technological delays. In modeling the electric energy consumption, 25 parameters were considered during melting (e.g., coke, dolomite, quantity), refining and tapping (e.g., injected oxygen, carbon, and limestone quantity) that were selected from 3248 consecutively produced batches in 2018. Two approaches were employed for the data analysis: linear regression and genetic programming model. The linear regression model was used in the first randomly generated generations of each of the 100 independent developed civilizations. More accurate models were subsequently obtained during the simulated evolution. The average relative deviation of the linear regression and the genetic programming model predictions from the experimental data were 3.60% and 3.31%, respectively. Both models were subsequently validated by using data from 278 batches produced in 2019, where the maintenance and the technological delays were below 20 minutes per batch. It was possible, based on the linear regression and the genetically developed model, to calculate that the average electric energy consumption could be reduced by up to 1.04% and 1.16%, respectively, in the case of maintenance and other technological delays.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2142-:d:237132
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    References listed on IDEAS

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    1. 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.
    2. Kovačič, Miha & Šarler, Božidar, 2014. "Genetic programming prediction of the natural gas consumption in a steel plant," Energy, Elsevier, vol. 66(C), pages 273-284.
    3. Oda, Junichiro & Akimoto, Keigo & Tomoda, Toshimasa & Nagashima, Miyuki & Wada, Kenichi & Sano, Fuminori, 2012. "International comparisons of energy efficiency in power, steel, and cement industries," Energy Policy, Elsevier, vol. 44(C), pages 118-129.
    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.
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    1. Yu-Chiao Lu & Liviu Brabie & Andrey V. Karasev & Chuan Wang, 2022. "Applications of Hydrochar and Charcoal in the Iron and Steelmaking Industry—Part 2: Carburization of Liquid Iron by Addition of Iron–Carbon Briquettes," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    2. 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).
    3. So-Won Choi & Bo-Guk Seo & Eul-Bum Lee, 2023. "Machine Learning-Based Tap Temperature Prediction and Control for Optimized Power Consumption in Stainless Electric Arc Furnaces (EAF) of Steel Plants," Sustainability, MDPI, vol. 15(8), pages 1-31, April.

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