JSC Kola MMC (Monchegorsk, Russia)
D. О. Sannikov, Director of the Department of Innovation and Digital, SannikovDO@kolagmk.ru
М. P. Marukhin, Chief Engineer of the Processing Plant of Kola MMC, MarukhinMP@kolagmk.ru
L. А. Brazyulis, Head of the Processing Laboratory of the Scientific and Technical Center, BrazyulisLA@kolagmk.ru
LLC Rocket Control (Moscow, Russia)
V. S. Kotenev, Chief Engineer of Customer Solutions Implementation, v.kotenev@rocketcontrol.ai
P. D. Prokhorov, Machine Learning-Based Optimization Solutions Development Engineer, p.prohorov@rocketcontrol.ai
I. V. Kalyakin, Technical Support Expert for Optimization Solutions, i.kalyakin@rocketcontrol.ai
The paper considers the issues of automating the control of flotation process operating conditions at mineral processing plants. The relevance of the study is based on the need to increase the recovery of non-ferrous metals from refractory raw materials through the improvement of process control systems. The aim of the study is to develop an optimization control algorithm for flotation feed density using intelligent control methods and industrial automation technologies. The study has employed methods of automatic control theory, elements of machine learning, and statistical processing of process data. An intelligent control system architecture has been developed to adapt control settings according to current process conditions. A digital optimization control algorithm has been proposed, which transmits water flow rate control setpoints to PID controllers and logic controllers. The scientific novelty lies in the development of a digital optimization control algorithm for pulp density that ensures stabilization of the process parameter within a specified range using a cascade scheme of “optimizer – PID controller – actuators” under conditions of variable ore composition. The practical significance of the study has been confirmed by industrial testing of the developed controller: the flotation feed density is maintained within the target range, creating the prerequisites for improving process stability and concentrate quality. The results can be used in the design of intelligent control systems for mineral processing operations.
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