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.

This article describes an approach of mathematical processing of signals (seismograms) from five blasthole charges from experimental blasting, each 3 m deep, with equal explosive weight (1 kg), and equidistant (3 m) from one other. The seismic explosive waves were measured at a 13 to 25 m distance. This article provides spectral analysis, wavelet analysis, and fractal analysis results. It defines the dependence of dominant frequency and amplitude on the distance to the blast center. According to the experimental data, the dominant frequency is calculated as y = 1.0262x0.2622 and the amplitude dependency as y = 18.139x−2.276. Furthermore, the analysis shows that 80% of the entire signal is concentrated in half the area of frequency range, i.e., the low frequency zone is of the most interest. This research defines the dependence of distance on the energy value of signal wavelet analysis. It is demonstrated that, according to the experimental data, the 12th frequency range is closely correlated with the distance values. This article gives the definitions of entropy, correlation dimension, and predictability time. This experiment shows that entropy and correlation dimension decrease but predictability time increases when the distance to the blast center increases. This article also describes the method for determining optimal drilling and blasting parameters, and concludes with the possibility of applying the analytical results to predicting and enhancing drilling and blast-ing operations.

Destruction of rocks and artificial composites is carried out in various ways. One of the most common methods is the impact fracture. The conical pick of JCB HM380 hydraulic breaker was used as the impact tool. The experimental study of rock fracture was carried out on an artificial composite—concrete. The depth of penetration of the pick into concrete was used as the test parameter. The model was based on the model of Professor V. B. Sokolinsky. The model developed in this work takes into account the degree of bluntness of the tool insingle and multiple impact. The system was considered as a two-component system due to the high stiffness of the pick–granite contact (as against the hammer piston–pick stiffness). The wave processes in the pick were disregarded because of the small length of the pick. The pick penetration depth in rocks was calculated theoretically, using the developed mathematical model, at each impact of the pick, and then was compared with the experimental data. The fracture tests also used JCB HM380 breaker and concrete. The penetration depth was determined from the step-frame analysis of video filming of the process. It is found that the pick blunting causes a monotonic increase in the rock resistance to the pick penetration, both in single and multiple impact, while the depth of the tool penetration and the blow time decrease (as compared with the initial state of the tool, by 28% and 20%, respectively).