Power Consumption Optimization for Electric Arc Furnace with Time Series Prediction

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 4, 2023
Jaehyuk Lee Songhwan Kim Boseon Yoo Jaesik Choi

Abstract

Optimizing power consumption for electric arc furnace (EAF) has a critical impact for maximizing productivity. To achieve the goal, we propose an AI based algorithm that determines optimal timing for recharging scrap to EAF. More specifically, we predict power consumption and time duration required for melting scrap considering scrap types and amounts of each type of scrap. Furthermore, with the advance in explainable AI, we offer guidance for the optimal timing of recharging scrap. We evaluate the performance on a real site and successfully reduce scrap charging time of 3% and power consumption of 7.1%, 53,802 Japanese Yen.

Abstract 137 | PDF Downloads 97

##plugins.themes.bootstrap3.article.details##

Keywords

EAF, Optimization, Cost-Saving, AI, Time-Series, XAI

References
Bisio, G., Rubatto, G., & Martini, R. (2000). Heat transfer, energy saving and pollution control in UHP electric-arc furnaces. Energy , 25 (11), 1047-1066.

Ameling, D., Strunck, F. J., Pottken, H. G., & Strohschein, H. (1983). Energy recovery from UHP electric arc furnaces using hot cooling. Steel and Energy , 353-374.

Nikolaev, A. A., Kornilov, G. P., Anufriev, A. V., Pekhterev, S. V., & Povelitsa, E. V. (2014). Electrical optimization of superpowerful arc furnaces. Steel in translation , 44 , 289-297.

MacRosty, R. D., & Swartz, C. L. (2005). Dynamic modeling of an industrial electric arc furnace. Industrial & engineering chemistry research , 44 (21), 8067-8083.

Yigit, C., Coskun, G., Buyukkaya, E., Durmaz, U., & Güven, H. R. (2015). CFD modeling of carbon combustion and electrode radiation in an electric arc furnace. Applied Thermal Engineering , 90 , 831-837.

Sandberg, E. (2005). Energy and scrap optimisation of electric arc furnaces by statistical analysis of process data (Doctoral dissertation, Luleå tekniska universitet).

Sandberg, E., Lennox, B., & Undvall, P. (2007). Scrap management by statistical evaluation of EAF process data. Control engineering practice , 15 (9), 10631075.

Wang, W. (2012). Cost optimization of scrap when making steel with an electric arc furnace.

Bai, E. W. (2014). Minimizing energy cost in electric arc furnace steel making by optimal control designs. Journal of Energy, 2014.

Gajic, D., Savic-Gajic, I., Savic, I., Georgieva, O., & Di Gennaro, S. (2016). Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks. Energy, 108, 132-139.

Choi, S. W., Seo, B. G., & Lee, E. B. (2023). Machine Learning-Based Tap Temperature Prediction and Control for Optimized Power Consumption in Stainless Electric Arc Furnaces (EAF) of Steel Plants. Sustainability , 15 (8), 6393.

Bae, S.C., Nam, J.S., & Moon, J.H. (2022). Current status of cement-concrete carbon neutrality in countries around the world. J. Concr. Soc. 2022, 34, 50–57.

Zhang, S., Yi, B., Guo, F., & Zhu, P. (2022). Exploring selected pathways to low and zero CO2 emissions in China’s iron and steel industry and their impacts on resources and energy. J. Clean. Prod. 2022, 340, 130813.

Yi, S. H., Lee, W. J., Lee, Y. S., & Kim, W. H. (2021). Hydrogen-Based Reduction Ironmaking Process and Conversion Technology. Korean Journal of Metals and Materials , 59 (1), 41-53.

Coskun, G., Sarikaya, C., Buyukkaya, E., & Kucuk, H. (2023). Optimization of the Injectors Position for an Electric Arc Furnace by using CFD Simulation. Journal of Applied Fluid Mechanics , 16 (2), 233-243.

Andonovski, G., & Tomažič, S. (2022). Comparison of data-based models for prediction and optimization of energy consumption in electric arc furnace (EAF). IFAC-PapersOnLine , 55 (20), 373-378.

Torquato, M. F., Martínez-Ayuso, G., Fahmy, A. A., & Sienz, J. (2021). Multi-objective optimization of electric arc furnace using the non-dominated sorting genetic algorithm II. IEEE Access , 9 , 149715149731.

Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., ... & Zhou, T. (2015). Xgboost: extreme gradient boosting. R package version 0.4-2 , 1 (4), 14.

Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica chimica acta, 185, 117.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.

Yoo, B., Lee, J., Ju, J., Chung, S., Kim, S., & Choi, J. (2021, July). Conditional temporal neural processes with covariance loss. In International Conference on Machine Learning (pp. 12051-12061). PMLR
Section
Special Session Papers