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ANALYTICAL METHODS IN BENEFICIATION PROCESSES
ArticleName Two options for calculating the technological balance of processing plants
DOI 10.17580/or.2024.03.05
ArticleAuthor Kozin V. Z., Komlev A. S., Vodovozov K. A.
ArticleAuthorData

Ural State Mining University (Ekaterinburg, Russia)
Kozin V. Z., Dean, Doctor of Engineering Sciences, Professor, gmf.dek@ursmu.ru
Komlev A. S., Senior Researcher, Candidate of Engineering Sciences, tails2002@inbox.ru
Vodovozov K. A., Senior Lecturer, gmf.opi@ursmu.ru

Abstract

Plant performance reports contain calculated data for processing plant indicators that cannot be directly measured and are widely used in industrial environments. This article discusses two available calculation options: compilation of a system of balance equations based on the relevant product testing data, and development of a mass balance equation based on the relevant weighing results. The quality of any plant performance data depends on the errors in establishing the known quantities included in the equations. In the first option, these include sampling errors for the ore, concentrate, and tailings, as well as ore amenability errors; in the second option, these are weighing errors for the ore and concentrates, as well as errors in establishing the masses of products in containers included in the performance data calculation scope. The most suitable performance data calculation option is selected depending on the magnitude and ratio of these errors specific to a particular processing plant. The two options are equivalent for a plant with high ore amenability and a simple system of balance equations (two to three equations) without a thickener in the calculation scope. For plants with low amenability and a more complex system of equations, the second option (a mass balance of the weighed products) is preferable. Only the second option should be used for plants with ore amenability indices of less than 2; with or without dry concentrate mass weighing in a thickener (for dry processes), it is advisable to use the second option at all plants. The relative random error formulas for the plant performance data calculation options allow selecting the applicable option for other situations beyond those discussed in the article.
The study was supported by the Ministry of Science and Higher Education of the Russian Federation (No. 0833-2023-0004) under the state assignment for the Ural State Mining University.

keywords Plant performance report, system of equations, mass balance, ore amenability, sampling error, thickener weighing, tailings mass, component mass
References

1. Kozin V. Z. Technological balance of processing plants. Ekaterinburg: UrSMU, 2017. 151 p.
2. Vodovozov K. A. Conditions for the feasibility of averaging the calculation results of technological beneficiation schemes for several components. Izvestiya Vuzov. Gornyi Zhurnal. 2012. No. 8. pp. 91–92.
3. Braun V. I., Dyumin V. G., Protsuto V. S., Milin I. M. Balance of metals. Computer calculations. Moscow: Nedra, 1991. 192 p.
4. Alenitsyn Yu. E., Braun V. I., Milin I. M. Engineering calculation of the technological balance for a two-product separation scheme. Obogashchenie Rud. 1978. No. 2. pp. 41–44.
5. Kozin V. Z., Komlev A. S. Metal balances at concentrators. Obogashchenie Rud. 2023. No. 2. pp. 9–15.
6. Morozov V. V., Stolyarov V. F., Konovalov N. M. Improving the efficiency of flotation management using in-line pulp composition analyzers. Obogashchenie Rud. 2003. No. 4. pp. 33–36.
7. Morozov V. V., Topchaev V. P., Ulitenko K. Ya., Gaanbaatar Z., Dalgerbat L. Development and application of automated control systems for mineral processing processes. Moscow: Ore & Metals Publishing House, 2013. 508 p.
8. Kroshkin A. M. Level measurement: microwaves or ultrasound? Avtomatizatsiya v Promyshlennosti. 2005. No. 2. pp. 29–33.
9. Topchaev V. P., Fedin G. V. Systems and means of control and management of technological units in beneficiation processes. Tsvetnye Metally. 2005. No. 10. pp. 77–79.
10. Ulitenko K. Ya. Some aspects of intellectual management of productivity and quality in iron ore beneficiation.
Obogashchenie Rud. 2006. No. 6. pp. 33–37.
11. Loy V. V. Automation of thickening process at No. 2 Hydrometallurgical plant: ways of solution. Tsvetnye Metally. 2009. No. 6. pp. 72–75.

12. Temerbekova B. M. Application of systematic error detection method to integral parameter measurements in
sophisticated production processes and operations. Tsvetnye Metally. 2022. No. 5. pp. 79–86.
13. Glazatov A. N., Molodtsev M. S., Kazakov A. M., Brazyulis L. A. Optimized product quality control at Kola MMC’s mineral processing plant. Tsvetnye Metally. 2020. No. 12. pp. 88–93.
14. Kibirev V. I. Tailing dump. St. Petersburg: «Mekhanobr Engineering», 2022. 312 p.
15. Babenko D. A., Pashkevich M. A. Study of the composition and properties of the copper ore processing tailings of PJSC Gaysky Mining and Processing Plant. Obogashchenie Rud. 2021. No. 2. pp. 47–51.
16. Batov A. A., Zadvorny V. A., Golubev D. G., Khavalits S. D. Ensuring the safety of products of Kola MMC JSC containing non-ferrous and precious metals. Tsvetnye Metally. 2020. No. 8. pp. 41–45.
17. Vodovozov K. A. Determination of the mass of the product in a container by the difference in the weights of the products entering and leaving it. Materials of the XXVI National scientific and technical conference «Scientific foundations and practice of processing ores and technogenic raw materials». May 26–27, 2021, Ekaterinburg. pp. 144–148.
18. Svensmark B. Extensions to the theory of sampling l. The extended Gy’s formula, the segregation paradox and fundamental sampling uncertainty (FSU). Analytica Chimica Acta. 2021. Vol. 1187. DOI: 10.1016/j.aca.2021.339127
19. Jean-Sebastian D., Esbensen K. H. Revisiting Pierre Gy’s formula (TOS) – A return to size-density classes for applications to contaminated soils, coated particular aggregated and mixed material systems. Analytica Chimica Acta. 2022. Vol. 1193. DOI: 10.1016/j.aca.2021.339227
20. Dominy S. C., Platten J. M., Glass H. J., Purevgerel S., Cuffley B. W. Determination of gold particle characteristics for sampling protocol optimization. Minerals. 2021. Vol. 11. pp. 1109–1155.
21. 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.
22. Henckens M. L. C. M., Worrell E. Reviewing the availability of copper and nickel for future generations. The balance between production growth, sustainability and recycling rates. Journal of Cleaner Production. 2020. Vol. 264. DOI: 10.1016/j. jclepro.2020.121460
23. Qi Ch.-Ch. Big data management in the mining industry. International Journal of Minerals, Metallurgy and Materials. 2020. Vol. 27, Iss. 2. pp. 131–139.

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