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Machine Learning-Based Tap Temperature Prediction and Control for Optimized Power Consumption in Stainless Electric Arc Furnaces (EAF) of Steel Plants

Author

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  • So-Won Choi

    (Graduate Institute of Ferrous and Energy Materials Technology, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea)

  • Bo-Guk Seo

    (Graduate Institute of Ferrous and Energy Materials Technology, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
    Control Technology Section, Electronic Instrument Control Department, Pohang Iron and Steel Company (POSCO), Pohang 37754, Republic of Korea)

  • Eul-Bum Lee

    (Graduate Institute of Ferrous and Energy Materials Technology, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
    Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea)

Abstract

The steel industry has been forced to switch from the traditional blast furnace to the electric arc furnace (EAF) process to reduce carbon emissions. However, EAF still relies entirely on the operators’ proficiency to determine the electrical power input. This study aims to enhance the efficiency of the EAF process by predicting the tap temperature in real time through a data-driven approach and by applying a system that automatically sets the input amount of power to the production site. We developed a tap temperature prediction model (TTPM) with a machine learning (ML)-based support vector regression (SVR) algorithm. The operation data of the stainless EAF, where the actual production work was carried out, were extracted, and the models using six ML algorithms were trained. The model validation results show that the model with an SVR radial basis function (RBF) algorithm resulted in the best performance with a root mean square error (RMSE) of 20.14. The SVR algorithm performed better than the others for features such as noise. As a result of a five-month analysis of the operating performance of the developed TTPM for the stainless EAF, the tap temperature deviation decreased by 17% and the average power consumption decreased by 282 kWh/heat compared with the operation that depended on the operator’s skill. In the results of the economic evaluation of the facility investment, the economic feasibility was found to be sufficient, with an internal rate of return (IRR) of 35.8%. Applying the developed TTPM to the stainless EAF and successfully operating it for ten months verified the system’s reliability. In terms of the increasing proportion of EAF production used to decarbonize the steel industry, it is expected that various studies will be conducted more actively to improve the efficiency of the EAF process in the future. This study contributes to the improvement of steel companies’ manufacturing competitiveness and the carbon neutrality of the steel industry by achieving the energy and production efficiency improvements associated with the EAF process.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6393-:d:1118777
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    References listed on IDEAS

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    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. Katsumasa Tanaka & Brian C. O’Neill, 2018. "The Paris Agreement zero-emissions goal is not always consistent with the 1.5 °C and 2 °C temperature targets," Nature Climate Change, Nature, vol. 8(4), pages 319-324, April.
    3. Chul-Seung Hong & Eul-Bum Lee, 2018. "Power Plant Economic Analysis: Maximizing Lifecycle Profitability by Simulating Preliminary Design Solutions of Steam-Cycle Conditions," Energies, MDPI, vol. 11(9), pages 1-21, August.
    4. So-Won Choi & Eul-Bum Lee & Jong-Hyun Kim, 2021. "The Engineering Machine-Learning Automation Platform ( EMAP ): A Big-Data-Driven AI Tool for Contractors’ Sustainable Management Solutions for Plant Projects," Sustainability, MDPI, vol. 13(18), pages 1-33, September.
    5. Wisnowski, James W. & Simpson, James R. & Montgomery, Douglas C. & Runger, George C., 2003. "Resampling methods for variable selection in robust regression," Computational Statistics & Data Analysis, Elsevier, vol. 43(3), pages 341-355, July.
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    1. Jin-Seong Choi & So-Won Choi & Eul-Bum Lee, 2023. "Modeling of Predictive Maintenance Systems for Laser-Welders in Continuous Galvanizing Lines Based on Machine Learning with Welder Control Data," Sustainability, MDPI, vol. 15(9), pages 1-28, May.

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