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Название Process control quality analysis
DOI 10.17580/tsm.2020.10.10
Автор Vasilieva N. V., Fedorova E. R.
Информация об авторе

Saint Petersburg Mining University, Saint Petersburg, Russia:

N. V. Vasilieva, Associate Professor at the Department of Industrial Automation, Сandidate of Technical Sciences, e-mail: vasileva_nv@pers.spmi.ru
E. R. Fedorova, Assistant Professor at the Department of Industrial Automation, Сandidate of Technical Sciences, e-mail: fedorova_er@pers.spmi.ru


All production processes are controlled by control systems, in which several information flows are generated. However, operators only use a small percent of this information as the processing capacity of a human mind is limited. The paper demonstrates that production process control is to a great extent influenced by human factor. The paper describes a production data processing technique that enables the personnel to make a better use of their resources for operations control. An approach is considered to studying metallurgical processes through analysis of indirect indicators – i.e. the spectral density and autocorrelation function of the key process indicator signals. A method is described to check the efficiency of material flow control systems. The above techniques were applied to a big array of monitoring data collected during a 24-hour smelting operation for copper-nickel sulphide material. Data from three different shifts were used for this analysis. Different operators have different control patterns. The proposed technique, which enables to analyze big arrays of monitoring data, helps minimize the human factor. The adopted experimental data processing technique helps interpret the obtained results for further practical use, development of new control algorithms and optimization of the current control system.

Ключевые слова Quality analysis, control quality, spectral density, autocorrelation function, production process control
Библиографический список

1. Kadyrov E. D., Danilova N. V. Analyzing the autogenous smelting process parameters. Avtomatizatsiya v promyshlennosti. 2008. No. 5. pp. 24–26.
2. Vasilieva N. V. Mathematical models for copper production control: Ideas, methods, examples. Moscow : Nauchno-izdatelskiy tsentr Infra-M, 2020. 194 p.
3. Spesivtsev A. V. Metallurgical process as an object of study: New concepts, consistency, practice. Saint Petersburg : Izdatelstvo Politekhnicheskogo universiteta, 2004. 306 p.

4. Fomin K. V. Method for estimating the spectrum density of the resistance moment on the working body of a peat milling unit. Zapiski Gornogo instituta. 2020. Vol. 241. pp. 58–67. DOI: 10.31897/PMI.2020.1.58.
5. Zykov I. E. A smart control system for Vanyukov furnace copper sulphide concentrate smelting process: Extended abstract of PhD dissertation. Moscow : 2008. 26 p.
6. Danilova N. V. A fuzzy control system for copper-nickel sulphide concentrate autogenous smelting process: Extended abstract of PhD dissertation. Saint Petersburg, 2010. 20 p.
7. Kostin E. V. Automated Vanyukov process product quality control: Extended abstract of PhD dissertation. Norilsk, 2013. 21 p.
8. Spesivtsev A. V. A smart system for Vanyukov furnace process control. Control engineering Russia. 2013. No. 4. pp. 86–91.
9. Vasilieva N. V., Fedorova E. R. Processing a big array of monitoring data and aggregating them to develop a process control system. Promyshlennye ASU i kontrollery. 2019. No. 3. pp. 3–9.
10. Vasilyeva N. V., Fedorova E. R. Statistical methods of evaluating quality of technological process control of trends of main parameters dependence. Journal of Physics: Conference Series. 2018. Vol. 1118. 012046. DOI: 10.1088/1742-6596/1118/1/012046.
11. Salikhov Z. G., Spesivtsev A. V., Moskvitin D. A., Sirichenko A. V., Zykov I. E. Metallurgical unit control quality: A quantitative analysis. Tsvetnye Metally. 2002. No. 10. pp. 89–92.
12. Box G., Kramer T. Statistical Process Monitoring and Feedback Adjustment – A Discussion. Technometrics. 1992. Vol. 34, Iss. 3. pp. 251–267.
13. Box G., Narasimhan S. Rethinking Statistics for Quality Control. Quality Engineering. 2010. Vol. 22. pp. 60–72.
14. Tynkachev A. R., Bukhner A. V. Introducing smart systems into production processes as a path towards efficiency. Promyshlennye ASU i kontrollery. 2006. No. 6. pp. 8–11.
15. Abrosimov A. A., Shelyago E. V., Yazynina I. V. Substantiation of a representative dataset on permeability to obtain valid petrophysical relationships. Zapiski Gornogo instituta. 2018. Vol. 233. pp. 487–491. DOI: .
16. Chernyshov S. E., Galkin V. I., Ulianova Z. V., Makdonald D. I. M. Developing mathematical models to control cement slurry parameters. Zapiski Gornogo instituta. 2020. Vol. 242. pp. 179–190. DOI: 10.31897/PMI.2020.2.179.
17. Kim J. S., Larsen M. D. Integration of Statistical Techniques into Quality Improvement Systems. Proceedings of the 41st Congress of the European Organization for Quality. 1997. Vol. 2. pp. 277–284.
18. Ustinov D. A., Baburin S. V. Influence by technological process onto mineral resources sector enterprise power supplies reliability parameters. International Journal of Applied Engineering Research. 2016. Vol. 11, Iss. 7. pp. 5267–5270.
19. Bazhin V. Y., Danilov I. V., Petrov P. A. Development of automated system based on neural network algorithm for detecting defects on molds installed on casting machines. Journal of Physics: Conference Series. 2018. No. 1015. pp. 1–6.
20. Aroian L. A., Levene H. The Effectiveness of Quality Control Charts. Journal of the American Statistical Association. 1950. Vol. 45. pp. 520–529.
21. Beloglazov I. I., Petrov P. A., Gorlenkov D. V. Development of an Algorithm for Control Metallurgical Processes of Fluidized Roasting Using an Adaptive Controller. Journal of Physics: Conference Series. 2018. Vol. 1059. p. 012015.
22. Zhukovskiy Y. L., Korolev N. A., Babanova I. S., Boikov A. V. The probability estimate of the defects of the asynchronous motors based on the complex method of diagnostics. 2017 IOP Conference Series Earth and Environmental Science. Vol. 87, Iss. 3. p. 032055. DOI: 10.1088/1755-1315/87/3/032055.

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