Название |
Complex control of the state of steel pins in Soderberg electrolytic cells by using computer vision systems |
Информация об авторе |
Saint-Petersburg Mining University, Saint Petersburg, Russia:
V. Yu. Bazhin, Dean of Chemical and Metallurgical Faculty, e-mail: bazhin-alfoil@mail.ru A. A. Kulchitskiy, Assistant Professor of a Chair of Automation of Technological Processes and Productions, e-mail: doz-ku@rambler.ru D. N. Kadrov, Post-Graduate Student of a Chair of Automation of Technological Processes and Productions |
Реферат |
Our study describes the problem of automated diagnostics of the state of anode steel pins in Soderberg electrolytic cells with a top current supply as it is the key component of metallurgic unit’s current lead. We proposed a non-contact control over the shape and dimensions of current lead pin’s working component by electro-optical projection technique and usage of a computer vision camera. The received combination of current lead pin’s sections images enables an estimation of its geometric parameters. The assessment is performed for 0.3 Мpx and 5 Мpx cameras with min and max resolution parameters, which are used in the majority of industrial computer vision systems. The investigation was performed on possibility of installing a remote control system to assess wear out of NOEL crane’s pins. The respective tasks of determining current lead pins’ geometry and controlling their instalment on the planned horizon in the anode forming were further combined. The contributing errors of an electro-optical current lead pin controlling system were determined. The main attention is paid to analysis of the system’s instrumental errors, such as background noise, object surface reflection, object positioning as regards the computer vision camera, computer vision camera position as regards the reference point — electrolytic cell, optical system aberration, optical medium refraction in workshop conditions with relevant numerical evaluation. To make the assessment more convenient, the results of accuracy analysis are specified in the table, enabling the estimation of determination errors: the shape of current lead pin and errors parameters, its radius and instalment on the planned horizon. The performed estimation allowed us to establish recommended parameters for a computer vision camera and its position to solve the task of a complex control of current lead pins in electrolytic cells with self-baking anodes. |
Библиографический список |
1. Trushko V. L., Utkov V. A., Bazhin V. Yu. Topicality and possibilities for complete processing of red mud of aluminous production. Zapiski Gornogo instituta. 2017. Vol. 227. pp. 547–553. 2. Les Edwards The History and Future Challenges of Calcined Petroleum Coke Production and Use in Aluminium Smelting. JOM. February 2015. Vol. 67, No. 2. pp. 308–321. 3. Panchenko S. V., Dli M. I., Bobkov V. I., Panchenko D. S. Problems of analysis of thermalphysic processes in a reaction zone of electrothermal reactor. Non-ferrous Мetals. 2017. № 1. pp. 36–42. DOI: 10.17580/nfm.2017.01.08 4. Panchenko S. V., Dli M. I., Bobkov V. I., Panchenko D. S. Certain of the thermal physics problems of reducing processes in chemical electrothermal reactors. Non-ferrous Мetals. 2017. № 1. pp. 43–48. DOI: 10.17580/nfm.2017.01.09 5. Pancnehko S. V., Dli M. I., Borisov V. V., Panchenko D. S. Analysis of thermalphysic processes in near-electrode zone of electrothermal reactor. Nonferrous Мetals. 2016. № 2. pp. 57–64. DOI: 10.17580/nfm.2016.12.12 6. Panchenko S. V., Meshalkin V. P., Dli M. I., Borisov V. V. Dynamics of thermophysical processes in electrothermal reducer. Non-ferrous Мetals. 2015. № 2. pp. 56–60. DOI: 10.17580/nfm.2015.02.10 7. Bazhin V. Yu., Savchenkov S. A., Kosov Yu. I. Specificity of the titaniumpowder alloying tablets usage in aluminium alloys. Non-ferrous Мetals. 2016. № 2. pp. 52–56. DOI: 10.17580/nfm.2016.12.11 8. Shirmohammadi S., Ferrero A. Camera as the instrument: the rising trend of vision based measurement. IEEE Transactions on Instrumentation and Measurement. 2014. Vol. 17 (3). pp. 41–47. 9. Bazhin V. Yu., Kulchitskiy A. A., Kadrov D. N., Petrov P. A. Optical monitorig of steel anode pins state on soderberg electrolyzers. Metallurg. 2016. No. 63 (4). pp. 96–100. 10. Abakumov I. I., Kulchitskiy A. A. Compensation of the Errors of a Passive Opto-Electronic System for Dimensional Control of Articles. Izmeritelnaya tekhnika. 2016. No. 8. pp. 27–30. 11. Zhang Z. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000. No. 22 (11). pp. 1330–1334. 12. Liu Zhen, Wu Qun, Chen Xu, Yin Yang. High-accuracy calibration of low-cost camera using image disturbance factor. Optics Express. 2016. Vol. 24, No. 21. pp. 24321–24336. 13. Abakumov I. I., Kulchitskiy A. A. Algorythmic method of error compensation in automated electronic system of control of geometric parameters of objects. Innovation systems of planning and management on transport and in mechanical engineering : collection of proceedings of the II International scientific and practical conference. Vol. II. Saint Petersburg : Natsionalnyy mineralno-syrevoy universitet “Gornyy”, 2014. pp. 104–107. 14. RD 50:48:0075.03.05. Guidelines on arrangement and safe exploitation of overhead gantry rail. 15. GOST 32579.5–2013. Cranes. Design principles for loads and load combinations. Part 5. Overhead type crane. Introduced: 01.06.2015. 16. Nikonov A. V. About the influence of vertical refraction on results of trigonometric levelling with short rays. Vestnik SGGA. 2014. No. 1 (25). pp. 12–26. 17. Martin J. A., Gross K. C. Estimating index of refraction from polarimetric hyperspectral imaging measurements. Optics Express. 2016. Vol. 24 (16). pp. 17928–17940. |