Control regularities of the useful mineral extraction from ore feed stream with autogenous grinding. Spectral analysis

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Authors:

I.K.Mladetsky, Dr. Sc. (Tech.), Prof., orcid.org/0000-0002-6159-6819, Dnipro University of Technology, Dnipro, Ukraine, e‑mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Ya.G.Kuvaiev, Cand. Sc. (Tech.), Assoc. Prof., orcid.org/0000-0003-4981-346X, Dnipro University of Technology, Dnipro, Ukraine, e‑mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

N.S.Pryadko, Dr. Sc. (Tech.), Senior Research Fellow, orcid.org/0000-0003-1656-1681, Dnipro University of Technology, Dnipro, Ukraine, e‑mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract:

Purpose. Summing up the results of a comprehensive study aimed at substantiating the fundamental decision-making base of the control channel of “useful mineral content in ore ‒ useful mineral content in concentrate” in automatic mode for plants, enriching poor iron-containing ores.

Methodology. Comparative analysis of objective indicators of the dispersion spectra, autocorrelation and correlation functions, technological variables, as well as amplitude-frequency characteristics of objects controlling the iron ore dressing processes and general provision synthesis of the mineral processing control system structure.

Findings. Substantiation was initiated regarding the fundamental decision-making base without the participation of the technological process operator based on the relationship between the many states of the control object and the final series of indicative events determined with the combined use of the correlation analysis method of the process variable values and object spectral analysis of iron ore dressing control. The control strategy of these objects, independent of the useful mineral liberation technology, has been determined.

Originality. It was shown for the first time that for all control objects of the poor iron-containing ores dressing, the automatic control system must be built according to the reaction on the deviation of the useful mineral content value at the control object output. For the first time, to substantiate the fundamental decision-making base, the finite series of indicative events was determined with the correlation and spectral analysis method, which are necessary but not sufficient to describe the set of states of the dressing objects of poor iron-containing ores in the control channel of “useful mineral content in ore ‒ useful mineral content in concentrate”.

Practical value. The obtained results can be the basis of a promising automatic control system of dressing technology objects for poor iron-containing ores.

References.

1. Enderev, V. A. (2012). The state and prospects of development of the management system of mining and processing industry in ferrous metallurgy. Modern research and innovation, 8. Retrieved from http://web.snauka.ru/issues/2012/08/16352.

2. Perrucci, M., Martin, M., & Ferreau, J. (2015). SmartMill_TM: Exceed your performance limits. Take full control of your grinding mills to increase productivity. ABB Ltd, Zurich, Switzerland. Retrieved from https://library.e.abb.com/public/1f0ebafac7e1475db7662c43c106dc03/WhitePaper_Smart%20Mill_LowRes.pdf.

3. Sibirtseva, N. B., Potapenko, A. N., & Semiletov, N. A. (2010). Methods of measuring the level of loading mills, based on the physical processes of modern centralized lubrication systems. News of the Samara Scientific Center of the Russian Academy of Sciences, 4(3), 694-697.

4. Andreev, E. E., Nikolaev, N. V., & Lviv, V. V. (2011). The method of automatic control of the operation of the mill autogenous. Notes of the Mining Institute, 192, 61-64.

5. Metso Group (2012). Optimizing the process with Metso CISA. Helsinki, Finland. Retrieved from http://www.madencilik-turkiye.com/dosyalar/metso/sunum7-julie_legay-prosesi_tamamlayici_uygulamalar.pdf.

6. Metso Group (2013). Minerals Processing Solutions. Automation. Helsinki, Finland. Retrieved from http://www.metso.com/miningandconstruction/mm_proj.nsf/WebWID/WTB-131030-2256F-1C5EA/$File/Metso%20Automation%202013.pdf.

7. OUTOTEC (2017). OUTOTEC automation solutions for grinding optimization. Espoo, Finland. – Retrieved from https://www.outotec.com/globalassets/products/analyzers-and-automation/ote_act_grinding_optimization_eng.pdf.

8. ANDRITZ (2015). Control systems for partial self-grinding mills. Graz, Austria. Retrieved from https://www.andritz.com/resource/blob/14846/87c53c36290cd5c943e75b006ba2f877/aa-brainwave-sagmill-ru-data.pdf.

9. OUTOTEC (2014). OUTOTEC Act Platform (2014). Espoo, Finland. Retrieved from https://www.outotec.com/globalassets/products/analyzers-and-automation/ote_act_platform_eng.pdf .

10. Mladetsky, I. K., Kuvaiev, I. G., & Priadko, N. S. (2018). The control regularity detection of the useful mineral extraction from the ore feed stream with autogenous grinding. Part 1. Correlation analysis. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 4(166), 59-65. DOI: 10.29202/nvngu/2018-4/6.

11. Mladetsky, I. K., Kuvaiev, I. G., & Priadko, N. S. (2017). Control regularities of the useful mineral extraction from ore feed stream with ball grinding. Spectral analysis. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu , 6(162), 132-138.

12. Mladetsky, I. K., Kuvaiev, I. G., & Priadko, N. S. (2017). Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu , 4(160), 91-98.

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ISSN (print) 2071-2227,
ISSN (online) 2223-2362.
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