Improving coordinated management of electric consumption by a crushing-enrichment complex of an enterprise

Authors

  • Valentin Khorolskyi Donetsk National University of Economics and Trade named after Michael Tugan-Baranowski Tramvayna str., 16, Kryvyi Rih, Ukraine, 50005, Ukraine
  • Dmitry Klyuev Donetsk National University of Economics and Trade named after Michael Tugan-Baranowski Tramvayna str., 16, Kryvyi Rih, Ukraine, 50005, Ukraine
  • Valentina Khotskina Kryvyi Rih Economic Institute of the Kyiv National Economic University named after Vadym Hetman Poshtoviy ave., 64, Kryvyi Rih, Ukraine, 50000, Ukraine
  • Dmitry Khorolskyi JSC «South Mining and Processing Plant» Kryvyi Rih, Ukraine, 50026, Ukraine

DOI:

https://doi.org/10.15587/1729-4061.2017.91768

Keywords:

electricity consumption, enrichment plants, ball mills, synchronous motors, exciters, adaptation, intelligentization

Abstract

We developed a strategy for coordinated management of power consumption by an enterprise with energy-intensive technological processes in the crushing and enrichment complex. An approach is presented to model power consumption by a complex based on the constructed causal model. We defined energy consumers at a crushing and enrichment complex, energy consumers – regulators and presented a characteristic of electric drives at an enrichment plant. A structure of integrated intelligent system is attached. It provides for the optimization of iron ore production in the periods of limited power supply from the energy system.

The system is integrated with the expert and operational subsystems and the management systems at the top level execution of business processes. The main task of expert and operating systems is the diagnosis of problematic situations, estimation of parameters of active, reactive powers and the prediction of output indicators of product quality. These innovations will enable the operating staff and the decision maker to receive in real time: predictive values of reactive, active values of capacity required to ensure planned indicators in production, predictive values of the trajectory in production. In the process of synthesis of control over power consumption by a crushing-enrichment complex, we defined a structure of artificial neural networks. During experimental research, we devised a procedure for training a neural network and the methods for learning. The scope of their application is separated in the system of coordinated situation management. A classification of production situations is established. We studied the modes of ball mills operation in the periods of limited power supply. A model for optimum loading of mills is presented. As a result of research, we developed: optimal setpoints for the exciters of ball mills' synchronous motors; production rules for the expert system to work out the fulfillment of planned production indicators over periods "peak", "half-peak", "night". The devised algorithms, intelligent systems of coordinated control over trajectories in power consumption and production have economic importance for enterprises in the mining-metallurgical sector of Ukraine. This is achieved by reducing specific electricity consumption by 5–7 % from the indicators in 2013, decreasing the losses in production during periods of limited power supply by 3 % from the indicators in 2013.

Author Biographies

Valentin Khorolskyi, Donetsk National University of Economics and Trade named after Michael Tugan-Baranowski Tramvayna str., 16, Kryvyi Rih, Ukraine, 50005

Doctor of Technical Sciences, professor

Department of general engineering disciplines and equipment

Dmitry Klyuev, Donetsk National University of Economics and Trade named after Michael Tugan-Baranowski Tramvayna str., 16, Kryvyi Rih, Ukraine, 50005

PhD, Associate Professor

Department of general engineering disciplines and equipment

Valentina Khotskina, Kryvyi Rih Economic Institute of the Kyiv National Economic University named after Vadym Hetman Poshtoviy ave., 64, Kryvyi Rih, Ukraine, 50000

PhD, Assistant Professor

Department of Informational Technologies

Dmitry Khorolskyi, JSC «South Mining and Processing Plant» Kryvyi Rih, Ukraine, 50026

Head master

Сrushing complex

References

  1. Horol's'kyj, V. P., Horol's'kyj, D. V., Titorenko, K. G. (2015). Bagatorivneva intelektual'na systema optymizacii' elektrospozhyvannjam girnycho-zbagachuval'nyh pidpryjemstv. Visnyk Hmel'nyc'kogo nacional'nogo universytetu. Tehnichni nauky, 2, 188–198.
  2. Kochura, Je. V., Garenko, A. A. (2011). Ekonomichnyj kryterij optymizacii' vytrat na elektroenergiju v procesah rudopidgotovky. Naukovyj visnyk Nacional'nogo girnychogo universytetu, 3, 122–124.
  3. Horol's'kyj, V. P., Horol's'kyj, D. V., Titorenko, K. G. (2015). Avtomatyzovana systema upravlinnja elektrospozhyvannjam zbagachuval'noi' fabryky pidpryjemstva girnycho – metalurgijnogo kompleksu. Visnyk Hmel'nyc'kogo nacional'nogo universytetu. Tehnichni nauky, 5, 86–92.
  4. Energetychna strategija Ukrai'ny na period do 2030 roku (2006). Kabinet Ministriv Ukrai'ny. Kyiv, No. 145-r.
  5. Shhokin, V. P., Kuz'menko, A. S., Shhokina, O. V. (2007). Metod korotkotryvalogo nejronechitkogo prognozu elektrospozhyvannja pidrozdilamy VAT «PivnGZK». Praci LV MAI, 1 (14), 84–89.
  6. Shchokin, V., Shchokina, O., Berezhniy, S. (2015). The example of application of the developed method of Neuro-Fuzzy rationing of power consumption at JSC "YuGOK" mining enrichment plants. Metallurgical and Mining Industry, 2, 19–26. Available at: https://www.metaljournal.com.ua/assets/Journal/english-edition/MMI_2015_2/004Bereznoi.pdf
  7. Dremin, A. A. (2006). Strategija jenergosberezhenija pri dobyche i pererabotke zheleznyh rud. Gornyj zhurnal, 12, 45–47.
  8. Faris Sameer Rasmi Alkhori., Alekseyev, M. O. (2014). Automated control of oze – pebble mill charge by the signal of active power of magnetic separator electric motor. Naukovyj visnyk Nacional'nogo girnychogo universytetu, 3, 71–76.
  9. Kochura, Je. V., Garenko, A. A. (2011). Ekonomiko – matematychne modeljuvannja pytomyh vytrat na elektroenergiju v procesi droblennja rudy. Naukovyj visnyk Nacional'nogo girnychogo universytetu, 57–63.
  10. Kornijenko, V. I. (2009). Ijerarhichne adaptyvne keruvannja procesamy rudopidgotovky za synergetychnym pryncypom z intelektual'nym prognozuvannjam. Naukovyj visnyk Nacional'nogo girnychogo universytetu, 11, 61–66.
  11. Grigor'ev, I. V., Shishkov, I. A. (2001). Lokal'nye zadachi izmerenij i vychislenij pri ispol'zovanii SCADA – OASyS. Promyshlennye ASU i kontrollery, 8, 37–41.
  12. Hanaev, V. V. (2008). Potrebiteli – reguljatory: vozmozhnosti i perspektivy primenenija. Nauchno – tehnicheskie vedomosti SPbGPU, 1, 59–63.
  13. Pravila ustrojstva jelektroustanovok (1998). Moscow: Glavgosjenergonadzor Rossii, 608.
  14. Stevenson, W. J. (1998). Production/Operations Management: With Standard Cd-Rom Package (Irwin/Mcgraw-Hill Series Operations and Decision Sciences). Richard D Irwin, 912.
  15. Khotskina, V. B. (2014). Automatization of management processes by the first stage of ore enrichment with the usage of quickly acting regressive models. Naukovyj visnyk Nacional'nogo girnychogo universytetu, 3, 145–151.
  16. Len'shin, V. N., Kuminov, V. V. Proizvodstvennye ispolnitel'nye sistemy (MES) – put' k jeffektivnomu predprijatiju. Sredstva i sistemy komp'juternoj avtomatizacii. Available at: http://asutp.ru/?p=600359
  17. Kharlamov, A. A.; Holden, A. V., Krynkov, V. I. (Eds.) (1991). Attention mechanism usage to form framework structures on a semantic net. Vol. 11. Neurocomputers and Attention. Connectionism and neurocomputers. – Manchester-New York: Manchester University Press, 747–756.
  18. Shubladze, A. M. (1984). Sintez optimal'nyh linejnyh reguljatorov. Avtomatika i telemehanika, 12, 22–23.

Downloads

Published

2017-02-27

How to Cite

Khorolskyi, V., Klyuev, D., Khotskina, V., & Khorolskyi, D. (2017). Improving coordinated management of electric consumption by a crushing-enrichment complex of an enterprise. Eastern-European Journal of Enterprise Technologies, 1(3 (85), 4–12. https://doi.org/10.15587/1729-4061.2017.91768

Issue

Vol. 1 No. 3 (85) (2017): Control processes

Section

Control processes

License

Copyright (c) 2017 Valentin Khorolskyi, Dmitry Klyuev, Valentina Khotskina, Dmitry Khorolskyi

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.