New approaches to improving efficiency of automated control over ore pretreatment process stages

The efficient use of mineral resources is a top priority objective in the mining and processing industry. The on-line testing of qualitative and quantitative characteristics of ore flows and the control over the whole product value chain are the obligatory conditions of effective production. It is expedient to develop proprietary hardware and software solutions to maintain profitability of operating mining and processing plants in the conditions of technological and economic isolation. This article discusses digital platforms designed by the world’s leading companies for the implementation of multilevel control over flow processes in mineral mining and processing. The authors propose a domestic alternative to implementing advanced approaches to upgrading process stages in ore pretreatment—intelligent control system RAZUM. The system uses the mathematical formalization of technological processes with regard to inaccuracy of laboratory measurements and material flows. The control involves multiparameter controllers of different processes by one or a few target parameters, and takes into account the dynamic wear of equipment as well as the change in the physicochemical properties of raw materials. The use of the digital twins of the process stages allows modeling different variants of operating modes of equipment and the whole value chain depending on the required quality of the final product and properties of the initial feedstock.

Keywords: mining industry, ore pretreatment, ore processing, automated system, digital twins, analytical systems, import substitution, material flow control, intelligent control system RAZUM.
For citation:

mining industry, ore pretreatment, ore processing, automated system, digital twins, analytical systems, import substitution, material flow control

Acknowledgements:
Issue number: 2
Year: 2024
Page number: 76-92
ISBN: 0236-1493
UDK: 622.23.05+622.6
DOI: 10.25018/0236_1493_2024_2_0_76
Article receipt date: 25.05.2023
Date of review receipt: 15.09.2023
Date of the editorial board′s decision on the article′s publishing: 10.01.2024
About authors:

A.S. Anufriev, Development Director, Engineering Laboratory LLC, 197341, Saint-Petersburg, Russia, e-mail: a@ануфриев.рф,
E.A. Lebedik1, Cand. Sci. (Eng.), Assistant Lecturer, e-mail: Lebedik_EA@pers.spmi.ru, ORCID ID: 0009-0008-5852-1411,
V.Yu. Bazhin1, Dr. Sci. (Eng.), Professor, e-mail: bazhin-alfoil@mail.ru, ORCID ID: 0000-0001-8231-3833,
1 Empress Catherine II Saint-Petersburg Mining University, 199106, Saint-Petersburg, Russia.

 

For contacts:

E.A. Lebedik, e-mail: Lebedik_EA@pers.spmi.ru.

Bibliography:

1. Lukichev S. V., Nagovitsyn O. V. Digital transformation and technological independence of the mining industry. Russian Mining Industry Journal. 2022, no. 5, pp. 74—78. [In Russ]. DOI: 10.30686/ 1609-9192-2022-5-74-78.

2. Matevosian R. A., Varfolomeyev I. A. Software for the analysis and control model of the sinter charge composition. Cherepovets State University Bulletin. 2022, no. 6 (111), pp. 65—78. [In Russ]. DOI: 10.23859/1994-0637-2022-6-111-5.

3. Kobzev V. V., Babkin A. V., Skorobogatov A. S. Digital transformation of industrial enterprises in the new reality. π-Economy. 2022, vol. 15, no. 5, pp. 7—27. [In Russ]. DOI: 10.18721/JE.15501.

4. Krakovskaya I. N. The concept of sustainable competitiveness of industrial clusters in Russia: the main provisions. Journal of Economics, Entrepreneurship and Law. 2023, no. 2, pp. 343—364. [In Russ]. DOI: 10.18334/epp.13.2.116984.

5. Rylnikov A. G., Pytalev I. A. Digital transformation of the mining industry: technical solutions and technological challenges. News of the Tula state university. Sciences of Earth. 2020, no. 1, pp. 470—481. [In Russ]. DOI: 10.46689/2218-5194-2020-1-1-470-481.

6. Tishchenko I. V., Vanag Yu. V. Automation and robotization of solid mineral mining. Interexpo Geo-Siberia. 2022, vol. 2, no. 3, pp. 325—333. [In Russ]. DOI: 10.33764/2618-981X-2022-2-3-325-333.

7. Zakharov V. N., Kubrin S. S. Digital transformation and intellectualization of mining systems. MIAB. Mining Inf. Anal. Bull. 2022, no. 5-2, pp. 31—47. [In Russ]. DOI: 10.25018/0236_1493_202 2_52_0_31.

8. Kantemirov V. D., Iakovlev A. M., Titov R. S. Applying geoinformation technologies of block modelling to improve the methods of assessing quality indicators of minerals. Izvestiya vysshikh uchebnykh zavedenii. Gornyi zhurnal. 2021, no. 1, pp. 63—73. [In Russ]. DOI: 10.21440/0536-1028-20211-63-73.

9. Jaskó S., Skorp A., Holczinger T., Chovan T., Abonyi J. Development of manufacturing execution systems in accordance with Industry 4.0 requirements. A review of standardand ontology-based methodologies and tools. Computers in Industry. 2020, vol. 123, no. 2, article 103300. DOI: 10.34218/ IJM.10.2.2019.010.

10. Rylnikova M. V., Klebanov D. A., Makeev M. A., Kadochnikov M. V. Application of artificial intelligence and the future of big data analytics in the mining industry. Russian Mining Industry Journal. 2022, no. 3, pp. 89—92. [In Russ]. DOI: 10.30686/1609-9192-2022-3-89-92.

11. Safiullin R. N., Afanasyev A. S., Reznichenko V. V. The concept of development of monitoring systems and management of intelligent technical complexes. Journal of Mining Institute. 2019, vol. 237, pp. 322—330. [In Russ]. DOI: 10.31897/pmi.2019.3.322.

12. Shestakov A. K., Petrov P. A., Nikolaev M. Y. Automatic system for detecting visible emissions in a potroom of aluminum plant based on technical vision and a neural network. Metallurgist. 2023, vol. 66, pp. 1308—1319. DOI: 10.1007/s11015-023-01445-z.

13. Kulchickiy A. A., Mansurova O. K., Nikolaev M. Yu. Recognition of defects in hoisting ropes of metallurgical equipment by an optical method using neural networks. Chernye metally. 2023, no. 3, pp. 81—88. [In Russ]. DOI: 10.17580/chm.2023.03.13.

14. Klebanov D. A., Makeev M. A. Digital advisers for the coal industry. Methodology of implementation. Ugol’. 2022, no. 8, pp. 112—115. [In Russ]. DOI: 10.18796/0041-5790-2022-8-112-115.

15. Stepanova I. V., Ivanovskaya A. N., Aksenova E. K., SHeklein A. A., Galoyan A. R., Minasyan A. G. Development of a simulation model of ore transportation of a mining and processing plant in the BPSIM system. Devyataya vserossiyskaya nauchno-prakticheskaya konferentsiya po imitatsionnomu modelirovaniyu i ego primeneniyu v nauke i promyshlennosti «Imitatsionnoe modelirovanie. Teoriya i praktika» (IMMOD-2019) [Proceedings of 9_th Russian Scientific and Practical Conference on Simulation Modeling and its Application in Science and Industry], Ekaterinburg, 2019, pp. 548—552. [In Russ].

16. Mohammed W. M., Ferrer B. R., Iarovyi S., Negri F., Fumagalli L., Lobov A., Lastra J. M. Generic plat form for manufacturing execution system functions in knowledge-driven manufacturing systems. International Journal of Computer Integrated Manufacturing. 2018, vol. 31, no. 3, pp. 262—274. DOI: 10.1080/0951192X.2017.1407874.

17. Deryabin S. A., Kondratev E. I., Rzazade Ulvi Azar ogly, Temkin I. O. Digital Mine architecture modeling language: Methodological approach to design in Industry 4.0. MIAB. Mining Inf. Anal. Bull. 2022, no. 2, pp. 97—110. [In Russ]. DOI: 10.25018/0236_1493_2022_2_0_97.

18. Vasilev B. Yu., Mustafin M. G. Digital relief models of open-pit mining facilities: Analysis and optimization. MIAB. Mining Inf. Anal. Bull. 2023, no. 9, pp. 141—159. [In Russ]. DOI: 10. 25018/ 0236_1493_2023_9_0_141.

19. Kantemirov V. D., Yakovlev A. M., Titov R. S., Timokhin A. V . Improvement of mineral processing methods in mining structurally-complex deposits. Russian Mining Industry Journal. 2022, no. S1, pp. 63—70. [In Russ]. DOI: 10.30686/1609-9192-2022-1S-63-70.

20. Shadrunov A. G., Sablyov S. A., Pytalev I. A., Fridrikhsonov O. V. Improvement of the Svetlinsky gold deposit logistics scheme with transition to cycle-flow technology. News of the Tula state university. Sciences of Earth. 2020, no. 4, pp. 535—547. [In Russ].

21. Negri E., Berardi S., Fumagalli L., Macchi M. MES-integrated digital twin frameworks. Journal of Manufacturing Systems. 2020, vol. 56, no. 6, pp. 58—71. DOI: 10.1016/j.jmsy.2020.05.007.

22. Aleksandrova T. N., Chanturiya A. V., Kuznetsov V. V. Mineralogical and technological features and patterns of selective disintegration of ferruginous quartzites of the Mikhailovskoye deposit. Journal of Mining Institute. 2022, vol. 256, pp. 517—526. [In Russ]. DOI: 10.31897/PMI.2022.58.

23. Golovina E. I., Grebneva A. V. Features of groundwater resources management in the transboundary territories (on the example of the Kaliningrad region). Geology and mineral resources of Siberia. 2022, no. 4, pp. 85—94. [In Russ]. DOI: 10.20403/2078-0575-2022-4-85-94.

24. Varlamova S. A., Volodina Yu. I., Zatonskiy A. V., Yazev P. A. Simulation model for planning mining operations. MIAB. Mining Inf. Anal. Bull. 2019, no. 10, pp. 214—222. [In Russ]. DOI: 10.25018/0236-1493-2019-10-0-214-222.

25. Saadoun A., Fredj M., Boukarm R., Hadji R. Fragmentation analysis using digital image processing and empirical model (KuzRam): a comparative study. Journal of Mining Institute. 2022, vol. 257, pp. 822—832. [In Russ]. DOI: 10.31897/PMI.2022.84.

26. Fedorova E., Pupysheva E., Morgunov V. Modelling of red-mud particle-solid distribution in the feeder cup of a thickener using the combined CFD-DPM approach. Symmetry. 2022, vol. 14, no. 11, article 2314. DOI: 10.3390/sym14112314.

27. Rozs R., Ando M. Collaborative systems, operation and task of the manufacturing execution systems in the 21st century industry. Periodica Polytechnica Mechanical Engineering. 2020, vol. 64, no. 1, pp. 51—66. DOI: 10.3311/PPme.14413.

28. Serbin S. D., Smirnova O. A. Creation of digital twins at mining enterprises. Tsifrovaya transformatsiya ekonomicheskikh sistem: problemy i perspektivy (EKOPROM-2022): Sbornik trudov Vserossiyskoy nauchno-prakticheskoy konferentsii s zarubezhnym uchastiem [Digital Transformation of Economic Systems: Problems and Prospects (ECOPROM-2022). Proceedings of the All-Russian scientific and practical conference with foreign participation], Saint-Petersburg, Politekh-Press, 2022, pp. 541—544. [In Russ]. DOI: 10.18720/IEP/2021.4/165.

29. Abburu S., Berre A., Jacoby M. COGNITWIN — hybrid and cognitive digital twins for the process industry. Conference: IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC). 2020. DOI: 10.1109/ICE/ITMC49519.2020.9198403.

30. Velikanov V. S., Bochkov V. S., Dyorina N. V., Bochkova K. V. Digital image processing for assessing the liner armor condition of cone crushers. MIAB. Mining Inf. Anal. Bull. 2022, no. 11-2, pp. 159—168. [In Russ]. DOI: 10.25018/0236_1493_2022_112_0_159.

31. Duarte R. A., Yamashita A. S., Silva M., Cota L. P., Euzébio T. M. Calibration and validation of a cone crusher model with industrial data. Minerals. 2021, vol. 11, no. 11, article 1256.

32. Moraes M. N., Galery R., Mazzinghy D. B. A review of process models for wet fine classification with high frequency screens. Powder Technology. 2021, vol. 394, pp. 525—532. DOI: 10.1016/j. powtec.2021.08.078.

33. Rodionov N. V., Zagidullin R. S. Analysys of expert methods innovation quality assessment. News of the Tula state university. Technical sciences. 2020, no. 10, pp. 105—111. [In Russ].

34. Arakelyan E. K., Andryushin A. V., Pashchenko F. F., Mezin S. V., Kosoy A. A. Problems and possibilities of integration of intellectual algorithms into application software of modern software and hardware complexes. Trudy 14-y mezhdunarodnoy konferentsii «Upravlenie razvitiem krupnomasshtabnykh sistem» (MLSD'2021) [Proceedings of 14th International Conference: Management of large-scale systems development (MLSD'2021)], Moscow, IPU RAN, 2021, pp. 808—814. [In Russ].

35. Ilyushin Y. V., Kapostey E. I. Developing a comprehensive mathematical model for aluminium production in a soderberg electrolyser. Energies. 2023, vol. 16, article 6313. DOI: 10.3390/en16176313.

Подписка на рассылку

Подпишитесь на рассылку, чтобы получать важную информацию для авторов и рецензентов.