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ArticleName Functional design of control systems designed for different processing lines of non-ferrous metallurgy (examples of implementation)
DOI 10.17580/tsm.2023.04.05
ArticleAuthor Kuzyakov A. V., Zhidovetskiy V. D., Kulchitskiy A. A., Rusinov L. A.

Soyuztsvetmetavtomatika JSC, Moscow, Russia:

A. V. Kuzyakov, Senior Researcher, e-mail: 31@sс


Kola Mining and Metallurgical Company, Monchegorsk, Russia:

V. D. Zhidovetskiy, Principal Specialist, Automation Department, Candidate of Technical Sciences


Saint Petersburg Mining University, Saint Petersburg, Russia:

A. A. Kulchitskiy, Head of the Department of Process and Plant Automation, Associate Professor, Doctor of Technical Sciences, e-mail:


Saint Petersburg State Institute of Technology (Technical University), Saint Petersburg, Russia:

L. A. Rusinov, Head of the Department of Chemical Process Automation, Professor, Doctor of Technical Sciences, e-mail:


This paper considers some examples of problem solving related to optimum automatic process control in nickel production implemented with the help of control unit VAZM-2U developed by Soyuztsvetmetavtomatika. It is noted that direct measurement of key parameters is not always possible. That’s why indirect parameters were used for object status evaluation, which have high-frequency interference and a time lag. Thus, special adaptive search algorithms were used for solving control problems. Such algorithms analyze process parameter trends. The paper considers the case study of an automatic control system that controls the water/converter matte ratio at the feed to the mill. The authors demonstrate how one can use the –45 μm output trend analysis to achieve the maximum possible output. This enables to achieve the best converter matte flotation performance. Another example would be the problem of automatic control over the size of nickel concentrate during fluidized bed roasting. Since there are no devices that could measure the size of material directly in the furnace, the paper demonstrates how using indirect data about the relationship between the particle size and the air pressure fluctuations in the air boxes underneath the furnace bottom, expressed as a regression equation of the relationship between the root-mean-square deviation of pressure fluctuations and the equivalent particle diameter, one can have a continuous analysis of the material size in the furnace. This enables to develop an automatic control system to control the coarsening of the fluidized bed particles.

keywords Grinding, filtration, blending, trend, automatic control, fluidized bed, pressure fluctuation, dispersion, equivalent diameter

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