Журналы →  Gornyi Zhurnal →  2023 →  №10 →  Назад

Название Experience of development, introduction and improvement of automated assaying system for ore pulp products
DOI 10.17580/gzh.2023.10.08
Автор Bondarenko A. V., Polishchuk A. M., Andreev I. V., Shapovalov E. V.
Информация об авторе

Analytical Center, RIVS Group, Saint-Petersburg, Russia

A. V. Bondarenko, Candidate of Engineering Sciences, Director, A_Bondarenko@rivs.ru
A. M. Polishchuk, Head of Experimental Development Sector
I. V. Andreev, Head of R&D Technical Support Sector
E. V. Shapovalov, Head of System Engineering Sector


The article proposes an automated pulp assaying system (APAS) which is a critical component of the integrated product quality control in the modern mining and processing industry. The reliable express-control of the pulp product composition, as well as solution of all problems connected with assaying requires both representative selection and representative preparation of samples of ore processing products being controlled. APAS is applicable in both on-line and balance assaying. The software and hardware of automated assaying system and sample preparation systems, their configurations and functions are described. It is emphasized that the general-purpose APAS (on-line, balance and final product assaying) can operate independently and within the proprietary automated analytical control system ASAK-RIVS based on the automated X-ray fluorescence pulp flow analyzer ARFA-RIVS. The developed equipment is compared with the foreign analogs within the framework of the state strategy of import substitution. The operating experience of the automated pulp assaying system allows stating that the system is reliable, needs no attendance and fully complies with the requirements of the mining and processing industry.

Ключевые слова Assaying, sampling, sample preparation, sample representativity, automated equipment systems, introduction, improvement, import substitution
Библиографический список

1. Kozin V. Z. Assaying of mineral raw materials. Yekaterinburg : UGGU, 2011. 316 p.
2. Karpenko N. V. Assaying and quality control of ore processing products. Moscow : Nedra, 1987. 215 p.
3. Bondarenko A. V. Experience in designing and development prospects of automatic analytical control system for mining and processing operations. Gornaya promyshlennost. 2021. No. S5-2. pp. 67–71.
4. GOST 14180–80. Ores and concentrates of non-ferrous metals. Methods of sampling and preparation of samples for chemical analysis and determination of moisture. Moscow : Standartinform, 2010. 19 p.
5. Zimin A. V., Bondarenko A. V. Trushin A. A., Zakharov П. А. Cross-section sampler. Patent RF, No. 153389. Applied: 14.10.2014. Published: 20.07.2015, Bulletin No. 20.
6. Bondarenko A. V., Karamyshev N. I., Katzman Ya.M. An approach to creation of methodological and mathematical support for automatic analytical control system for flotation processes of ore concentration. Gornaya promyshlennost. 2021. No. S5-2. p. 72-77
7. Szaloki I., Racz G., Germany A. Fundamental parameter model for quantification of total reflection x-ray fluorescence analysis. Spectrochimica Acta Part B: Atomic Spectroscopy. 2019. Vol. 156. pp. 33–41.
8. Bowers C. Matrix effect corrections in X-ray fluorescence spectrometry. Journal of Chemical Education. 2019. Vol. 96, No. 11. pp. 2597–2599.
9. Du K. L., Swamy M. N. S. Neural Networks and Statistical Learning. 2nd edition. London : Springer-Verlag, 2019. 955 p.
10. Zeng Y., Zhang M., Han F., Gong Y., Zhang J. Spectrum analysis and convolutional neural network for automatic modulation recognition. IEEE Wireless Communications Letters. 2019. Vol. 8, No. 3. pp. 929–932.
11. Charu C. Aggarwal Neural Networks and Deep Learning : A Textbook. Springer, 2018. 497 p.
12. Khaikin S. Neural networks. Complete course. 2nd revised edition. Moscow, Saint-Petersburg : Dialektika, 2020. 1103 p.
13. De Bragança Pereira B., Rao C. R., de Oliveira F. B. Statistical Learning Using Neural Networks. A Guide for Statisticians and Data Scientists with Python, London : CRC Press, 2020, pp. 131–150.
14. Loimi J., Minkkinen P., von Alfthan C., Lohilanhti J., Korpela T. Evaluation of sampling error sources in a multiple cutter metallurgical sampler. TOS Forum. 2015. pp. 138–143. DOI: 10.1255/tosf.64
15. Get reliable metallurgical composite samples that represent the variations that occur in particle size distribution, elemental concentrations and solids flow. Metso. Available at: https://www.metso.com/portfolio/metallurgical-account-sampling (accessed: 19.07.2023).
16. Improve process control while reducing assaying and sampling costs. Our gravity-type samplers provide a continuous sample flow for online analysis that is proportional to changes in the properties of the process stream. Metso. Available at: https://www.metso.com/portfolio/primary-samplers-for-gravity-flow (accessed: 19.07.2023).
17. Enable monitoring of changes in the properties of a process stream with Metso pressure-type samplers. Metso. Available at: https://www.metso.com/portfolio/primary-samplers-for-pressurized-process-flows (accessed: 19.07.2023).
18. Zimina A. A., Bondarenko A. V., Trushin A. A., Zakharov P. A. Automatic sample preparation system. Patent RF No. 2710333; Applied: 18.03.2019; Published: 25.12.2019, Bulletin No. 36.

Language of full-text русский
Полный текст статьи Получить