АО «Союзцветметавтоматика им. Топчаева В. П.», Москва, Россия:

Е. А. Оксенгойт, заведующий лабораторией, канд. техн. наук, эл. почта: Okseng37@mail.ru

Н. А. Куницкий, инженер, эл. почта: Kunickiy00@mail.ru

Санкт-Петербургский горный университет, Санкт-Петербург, Россия:

П. А. Петров, декан факультета переработки минерального сырья, канд. техн. наук, эл. почта: Petrov_PA3@pers.spmi.ru

А. К. Шестаков, аспирант кафедры автоматизации технологических процессов и производств, эл. почта: s195017@stud.spmi.ru

Приведены данные о новых, не имеющих аналогов разработках АО «СоюзЦМА». Представлено описание приборов, предназначенных для определения концентрации аэрозолей серной кислоты и гидроксида натрия в воздухе рабочей зоны промышленных предприятий. Отражена ситуация, вызванная необходимостью разработки эталонного генератора для проведения испытаний. Приведены описания и особенности разработанных и включенных в Госреестр генератора ГРАНТ-А и cигнализатора ГРАНТ-КЩ. Показана важность решения проблемы контрол я потенциальных ситуаций, связанных с разливом серной кислоты. Был создан сигнализатор разлива серной кислоты САКС-1. Действие прибора основано на измерении тока, протекающего через чувствительный элемент, или потенциала, возникающего на его электроде. Сигнализатор САКС-1 состоит из блока управления и преобразователей с электрохимическими датчиками, помещенными в специальные адаптеры. Число преобразователей зависит от количества потенциальных точек утечки и составляет от 1 до 8. Прибор включен в Госреестр РФ и его поставляют на многие предприятия.

1. Oksengoyt E. A., Borisov B. N., Fokina E. Yu. Mutlifunctional Grant series gas detector for monitoring the workspace air. Tsvetnye Metally. 2005. No. 10. Special issue. pp. 28–30.
2. Prishchepov F. A. Detecting aerosols of caustic alkali: Сandidate of Technical Sciences dissertation. Ufa State Petroleum University. Ufa, 2019.
3. Atmospheric pollution monitoring: A 2018 progress review. Central Geophysical Observatory. St Petersburg, 2019.
4. Johnson J., Coty N. Jen. A sulfuric acid nucleation potential model for the atmosphere. 2022, Jan. DOI: 10.5194/acp-2022-35
5. Bersenev S. A., Gryazin V. I. Physics of atmospheric aerosols. Lecture course. Yekaterinburg : Izdatelstvo Uralskogo universiteta, 2008. 228 p.
6. Sulphuric acid chemist’s handbook. Ed. by K. M. Malin. 2nd edition. Moscow, 1971. 744 p.
7. Vasiliev B. T., Otvagina M. I. Sulphuric acid technology. Moscow, 1985. 384 p.
8. Guidelines on measuring the concentration of hazardous substances in the workspace air. Moscow, 1994.
9. Sulfuric acid. The Essential Chemical Industry. Available at: https://www.essentialchemicalindustry.org/chemicals/sulfur.html (Accessed: 16.02.2023).
10. Williams M. M., Loyalka S. K. Aerosol science: theory and practice. Oxford : Pengamon press, 1991. 463 p.
11. Sheldon K. Friedlander. Smoke, dust and haze. Fundamentals of aerosol dynamics. New York, Oxford, 2000. 431 p.
12. Litvinenko V. S., Petrov E. I., Vasilevskaya D. V., Yakovenko A. V. et al. Analyzing the role of the state in the mineral resources management. Journal of Mining Institute. 2023. Vol. 259. pp. 95–111. DOI: 10.31897/PMI.2022.100
13. Bazhin V., Masko O. Monitoring of the behaviour and state of nanoscale particles in a gas cleaning system of an ore-thermal furnace. Symmetry. 2022. Vol. 14. DOI: 10.3390/SYM14050923
14. Dubovikov O. A., Beloglazov I. I., Alekseev A. A. Specific features of the use of pulverized coal fuel in combined chemical processing. Obogashchenie Rud. 2022. No. 6. pp. 32–38. DOI: 10.17580/or.2022.06.06
15. Milla Gravalos E. Introduction to physics of aerosols. 2^nd ed. Madrid : Edition personal, 2003.
16. Pshenin V., Liagova A., Razin A., Skorobogatov A. et al. Robot crawler for surveying pipelines and metal structures of complex spatial configuration. Infrastructures. 2022. Vol. 7. p. 75. DOI: 10.3390/infrastructures7060075
17. RUSAL Sustainability Report 2020. Available at: https://rusal.ru/sustainability/report/ (Accessed: 14.12.2022).
18. Best available techniques (BAT) reference document for the non-ferrous metals industries. Aluminium production in Russian Federation (2019). Available at: http://burondt.ru/index/its-ndt.html (Accessed: 04.04.2022)
19. The program for improving the environmental efficiency of the Branch Office PJSC RUSAL Bratsk in Shelekhov (2019). Available at: https://minpromtorg.gov.ru/common/upload/docVersions/5defb7bbf31ef/actual/ppa_11_compressed.pdf (Accessed: 04.04.2022).
20. Oksengoyt E. A., Borisov B. N., Fokina E. Yu., Shipatov V. T. New devices for air emissions control in the working area of enterprises and their standards for settings and checking. Tsvetnye Metally. 2015. No. 9. pp. 42–47. DOI: 10.17580/tsm.2015.09.06
21. Oksengoyt-Gruzman E. A., Fokina E. Yu., Borisov B. N., Fokin M. Yu. Soyuztsvetmetavtomatika safeguarding the environment. Tsvetnye Metally. 2021. No. 3. pp. 22–27.
22. Borovikov S. M., Tsyrelchuk I. N., Troyan F. D. Reliability calculation for radioelectronic devices. Minsk, Belarusian State University of Informatics and Radioelectronics, 2010. 68 p.
23. Kalinchak V. V., Kontush S. M., Chernenko A. S., Shchekatolina S. A. Applied physics of aerosols: Learner’s guide. Odessa : Odesskiy natsionalnyi universitet im. Mechnikova, 2015. 130 p.
24. Oksengoyt-Gruzman E. A., Soloviev Yu. F., Borisov B. N., Shipatov V. T. Sulphuric acid detector. Patent RF, No. 75749. Applied: 29.02.2008. Published: 20.08.2008.
25. Kashin D. A., Kulchitskiy A. A. Image-based quality monitoring of metallurgical briquettes. Tsvetnye Metally. 2022. No. 9. pp. 92–98. DOI: 10.17580/tsm.2022.09.13
26. Zakharov L., Martyushev D., Ponomareva I. N. Predicting dynamic formation pressure using artificial intelligence methods. Journal of Mining Institute. 2022. Vol. 253. pp. 23–32. DOI: 10.31897/PMI.2022.11
27. Boikov A., Payor V., Savelev R., Kolesnikov A. Synthetic data generation for steel defect detection and classification using deep learning. Symmetry. 2021. Vol. 13. DOI: 10.3390/sym13071176
28. Mann V., Buzunov V., Pingin V., Zherdev A. et al. Environmental Aspects of UC RUSAL’s Aluminum Smelters Sustainable Development. Light Metals. 2019. pp. 553–563. DOI: 10.1007/978-3-030-05864-7_70
29. Zherdev A., Svoevskiy A., Pingin V., Shakhmatov V. et al. Environmental enhancement of potroom processes by using a machine vision system. Light Metals. 2022. pp. 979–984. DOI: 10.1007/978-3-030-92529-1_127
30. Non-destructive testing systems: How they help produce on continuous production lines. Available at: https://habr.com/ru/company/severstal/blog/567516/ (Accessed: 15.01.2023).
31. Vasilyeva N. V., Boikov A. V., Erokhina O. O., Trifonov A. Y. Automated digitization of radial charts. Journal of Mining Institute. 2021. Vol. 247. pp. 82–87. DOI: 10.31897/pmi.2021.1.9
32. Vasilyeva N., Fedorova E., Kolesnikov A. Big data as a tool for building a predictive model of mill roll wear. Symmetry. 2021. Vol. 13. DOI: 10.3390/sym13050859
33. 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. DOI: 10.3390/sym14112314
34. Cabascango V. E. Q., Bazhin V. Y., Martynov S. A., Pardo F. R. O. Automatic control system for thermal state of reverberatory furnaces in production of nickel alloys. Metallurgist. 2022. Vol. 66. pp. 104–116. DOI: 10.1007/S11015-022-01304-3
35. Awrejcewicz J., Oikonomou V. K., Boikov A., Payor V. The present issues of control automation for levitation metal melting. Symmetry. 2022. Vol. 14. DOI: 10.3390/sym14101968
36. Martynov S. A., Masko O. N., Fedorov S. N. Innovative ore-thermal furnace control systems. Tsvetnye Metally. 2022. No. 4. pp. 87–94. DOI: 10.17580/TSM.2022.04.11
37. Beloglazov I. I., Sabinin D. S., Nikolaev M. Yu. Modeling the disintegration process for ball mills using DEM. MIAB. Mining Informational and Analytical Bulletin. 2022. No. 6–2. pp. 268–282. DOI: 10.25018/0236_1493_2022_62_0_268
38. Nguyen H. H., Bazhin V. Y. Optimization of control system for electrolytic copper refining with digital twin during dendritic precipitation. Metallurg. 2023. No. 1. pp. 49–56. DOI: 10.52351/00260827_2023_01_49
39. Chen L. C. et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE Computer Society. 2018. Vol. 40, No. 4. pp. 834–848. DOI: 10.1109/TPAMI.2017.2699184
40. Krizhevsky A., Sutskever I., Hinton G. E. ImageNet classification with deep convolutional neural networks. Communications of the ACM. 2017. Vol. 60, No. 6. pp. 84–90. DOI: 10.1145/3065386
41. He K., Zhang X., Ren S., Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. pp. 770–778. DOI: 10.1109/CVPR.2016.90
42. Paszke A., Gross S., Massa F., Lerer A. et al. PyTorch: An imperative style, high-performance deep learning library. 33 Conference on Neural Information Processing Systems. Vancouver. 2019.
43. Chen C., Chen Q., Xu J., Koltun V. Learning to see in the dark. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. pp. 3291–3300. DOI: 10.1109/CVPR.2018.00347
44. Zhao S., Xie Y., Yue W., Chen X. A machine learning method for state identification of superheat degree with flame Interference. 10^th International Symposium on High-Temperature Metallurgical Processing. 2019. pp. 199–208. DOI: 10.1007/978-3-030-05955-2_19
45. Töreyin B. U., Çetin A. E. Online detection of fire in video. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2007. DOI: 10.1109/CVPR.2007.383442
46. New automation means and systems in mining, metallurgical and environment sectors: Proceedings of the 5^th international conference. Moscow, October 2022. Available at: http://www.scma.ru/ru/products/Material%20seminar.pdf (Accessed: 20.11.2022).
47. Soyuztsvetmetavtomatika. Available at: http://www.scma.ru/ru/products/index.html (Accessed: 01.03.2023).