The research is dedicated to the development of special devices (capsules) that can be used to control the mining ore behavior in the technological unit in order to increase processes efficiency. In the first part of the article, the choice of the discrete element method for gen-erating various particle trajectories in the unit (drum pelletizer) was substantiated. This part describes the specific technologies that were used to recognize the pelletizing mode. In par-ticular, conversation of paths to sensor readings is implemented using the Matlab Sensor Fusion and Tracking Toolbox. The obtained readings were processed using two neural network classifiers (DNN and LSTM). As a result, stable models for recognizing the pelletizing modes of the unit were obtained. LSTM recognition accuracy is greater than DNN. The developed approach can be used to recognize the operating modes of other technological units. In addition, data on particles trajectories can be used to improve DEM models of technological processes. Future work consists of the capsule physical implementation and testing the recognition algorithm on a real unit.

Any technological process including metallurgical processes is supported by the control system operation, accompanied by large information flows to be formed. However, the most part of this information is not used by specialists due to restricted capabilities of a human being that is, in fact, incapable of processing such information flows. It has been demonstrated that process control is significantly influenced by the human factor. This paper contains a methodology to process production information that permits the process personnel to use its potential in a more rational way to control the process. An approach has been reviewed to studying metallurgical processes using the analysis of indirect indicators of process management, namely the spectral density and auto-correlation function of main process parameters. A method to separate useful signals from noise has been studied. A method has been given to check the ACS management efficiency using primary material flows. The adopted methodology for processing experimental data permits interpreting the obtained results for their further practical application to develop new algorithms for process control and improve the existing control system.

Mining enterprises are widely introducing digital technologies and automation is one of such tools. Granularity monitoring, namely, the size determination of rock mass pieces is a common operational component of the processes that extract minerals by open-pit mining. The article proposes an approach that, in addition to the lump size distribution, makes it possible to estimate the lump form distribution as well. To investigate the effectiveness of monitoring the form of blasted rock mass lumps, the authors conducted experiments in four stages related to the rock condition. They include geological occurrence, explosive crushing, trommelling, and mill crushing. The relationship between these stages is presented and the change in the lumps fragment form is traced. The present article proposes an informational and analytical model of the processes at mining enterprises, extracting minerals by open-pit mining, as well as an algorithm for determining the lumps form and obtaining their distribution in the rock mass.