Simulation of Energy Consumption Processes at the Metallurgical Enterprises in the Energy-Saving Projects Implementation | Journal of Engineering Sciences

Simulation of Energy Consumption Processes at the Metallurgical Enterprises in the Energy-Saving Projects Implementation

Author(s): Kiyko S. G.1, Druzhinin E. A.2, Prokhorov O. V.2, Haidabrus B. V.3,4*

Affiliation(s): 
1 PJSC “Electrometallurgical plant “Dniprospetsstal” named after A. M. Kuzmin, Zaporizhzhya, Ukraine;
2 National Aerospace University, Kharkiv Aviation Institute “KhAI”, 17, Chkalova St., 61000 Kharkiv, Ukraine;
3 Riga Technical University, 1, Kalku St., LV-1658 Riga, Latvia;
4 Riseba University of Applied Science, 3, Meza St., LV-1056 Riga, Latvia.

*Corresponding Author’s Address: haidabrus@gmail.com

Issue: Volume 7, Issue 2 (2020)

Dates:
Paper received: July 6, 2020
The final version of the paper received: September 20, 2020
Paper accepted online: October 4, 2020

Citation:
Kiyko S. G., Druzhinin E. A., Prokhorov O. V., Haidabrus B. V. (2020). Simulation of energy consumption processes at the metallurgical enterprises in the energy-saving projects implementation. Journal of Engineering Sciences, Vol. 7(2), pp. G1–G11, doi: 10.21272/jes.2020.7(2).g1

DOI: 10.21272/jes.2020.7(2).g1

Research Area:  CHEMICAL ENGINEERING: Energy Efficient Technologies

Abstract. The features of improving energy efficiency at a metallurgical enterprise based on portfolio management of energy-saving projects are considered. A simulation model of energy consumption at the metallurgical enterprise, which covers the entire metal products manufacture process, has been developed. The parameters, conduct, and visualization of simulation models of the main equipment such as an electric arc furnace and a ladle furnaces are described. With this software package’s help, a comparison of the permissible values and the adjusting of the predicted consumption of active power by a metallurgical enterprise for each fixed point in time are carried out. The system calculates the operating mode regulation range of electric arc furnaces to ensure the continuity of steel casting during melting of a particular steel grade along the appropriate technological routes. The model likewise includes algorithms for transport equipment management that minimize disruptions in continuous casting machines’ operation and simulate emergencies. The analysis of the results of energy consumption processes simulation at the metallurgical enterprise is carried out. As a result of modeling, it was possible to increase the productivity of a group of electric arc furnaces and ladle furnaces and reduce the maximum consumption of active power by the metallurgical enterprise. Experimental studies of energy consumption planning methods have been carried out based on real data on the metal products manufacture and electrical energy consumption by the production units of PJSC “Electrometallurgical plant “Dniprospetsstal”. The use of the electrical energy consumption model allows in an integrated manner and responds to the dynamics of production processes to carry out further calculations of economic feasibility studies, analysis, and selection of options for the project’s implementation of an energy-saving portfolio at the metallurgical enterprise.

Keywords: energy efficiency, energy-saving projects, metallurgical enterprise, energy consumption model, forecast, electric arc furnace.

References:

  1. Kivko S. G., Druzhinin E. A., Prokhorov O. V., Kritsky D. N. (2019). Management of energy saving project and programs at metallurgical enterprises. 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT), Lviv, Ukraine, pp. 158-161, doi: 10.1109/STC-CSIT.2019.8929807.
  2. Efremkova T. I. (2017). Evaluation of electricity consumption planning efficiency in technological process of a steel-making plant. Modern Management Technology, Vol. 5 (77), available at: https://sovman.ru/en/article/7701/
  3. Bianco V., Manca O., Nardini S. (2009). Electricity consumption forecasting in Italy using linear regression models. Energy, No. 34, pp. 1413-1421, doi: 10.1016/j.energy.2009.06.034.
  4. Dordonnat V., Koopman S. J., Ooms M. (2012). Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modeling. Computational Statistics and Data Analysis, No. 56, pp. 3134-3152, doi: 10.1016/j.csda.2011.04.002.
  5. Yujuan R., Bao H. (2016). Modeling and Simulation of Metallurgical Process Based on Hybrid Petri Net. IOP Conference Series: Materials Science and Engineering, 157, doi: htpps://doi.org/10.1088/1757-899X/157/1/012018.
  6. Xueying W., Zhuchao Y., Pengfei X., Gaixia C., Shiyang L., Jing L., Yuanzheng Z. (2020). An Energy Consumption Prediction LSTM Model of Metallurgy Enterprises. IOP Conference Series: Earth and Environmental Science, Vol. 495, 012014, doi: 10.1088/1755-1315/495/1/012014.
  7. Rakhmonov I., Berdishev A., Niyozov N., Muratov A., Khaliknazarov U. (2020) Development of a scheme for generating the predicted value of specific electricity consumption. IOP Conference Series: Materials Science and Engineering, Vol. 883, 012103, doi: 10.1088/1757-899X/883/1/012103.
  8. Kornilov G. P., Nikolaev A. A., Yachikov I. M., Karandaev A. S., Yakimov I. A. (2017). Automatic Control of Arc Steel-Making Furnace Power Mode Based on Active Power Uniformity. Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control, Radio Electronics, Vol. 17, No. 4, pp. 122–133. doi: 10.14529/ctcr170413.
  9. Carlsson L. S., Samuelsson P. B., Jönsson P. G. (2020). Using Statistical Modeling to Predict the Electrical Energy Consumption of an Electric Arc Furnace Producing Stainless Steel. Metals Vol. 10(1), 36, doi: 10.3390/met10010036
  10. Chen C., Liu Y., Kumar M., Qin J. (2018). Energy Consumption Modelling Using Deep Learning Technique – A Case Study of EAF. Procedia CIRP, Vol. 72, pp. 1063-1068, doi: 10.1016/j.procir.2018.03.095.
  11. Nikolaev A. A., Tulupov P. G., Tulupova O. V., Lesher O. V. (2020). Mutual Influence of the Melting Stage and Electric Arc Current Harmonic Composition in Different Types of Electric Arc Furnaces. International Journal of Computing and Digital Systems, Vol. 9, No. 1, p. 1.
  12. Shyamal S., Swartz C. L. E. (2018). Real-Time Dynamic Optimization-Based Advisory System for Electric Arc Furnace Operation. Industrial & Engineering Chemistry Research, Vol. 57(39), pp. 13177-13190, doi: 10.1021/acs.iecr.8b02542.
  13. Carabalí D. M., Forero C. R., Cadavid Y. (2018). Energy diagnosis and structuring an energy saving proposal for the metal casting industry: An experience in Colombia. Applied Thermal Engineering, Vol. 137, pp. 767-773, doi: 10.1016/j.applthermaleng.2018.04.012.
  14. Molokanova V. M., Orliuk O. P., Petrenko V. O., Butnik-Syverskyi O. B., Khomenko V. L. (2020). Formation of metallurgical enterprise sustainable development portfolio using the method of analyzing hierarchies. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, No. 2, pp. 131-136, doi: 10.33271/nvngu/2020-2/131.
  15. Backman J., Kyllönen V., Helaakoski H. (2019). Methods and Tools of Improving Steel Manufacturing Processes: Current State and Future Methods. IFAC-PapersOnLine, Vol. 52(13), pp. 1174-1179, doi: 10.1016/j.ifacol.2019.11.355.
  16. Kiyko S. (2020). Predictive adaptation methodology for portfolio management of energy saving projects at metallurgical enterprises. Information Processing Systems, Issue 3(162), pp. 52-64, doi: 10.30748/soi.2020.162.06.

Full Text



© 2014-2021 Sumy State University.
"Journal of Engineering Sciences"
ISSN 2312-2498 (Print), ISSN 2414-9381 (Online).
All rights reserved.