There is a tendency to increase the use of demand response technology in the Russian Federation along with other developing countries, covering not only large industries, but also individual households and organizations. Reducing peak loads of electricity consumption and increasing energy efficient use of equipment in the power system is achieved by applying demand management technology based on modeling and predicting consumer behavior in an educational institution. The study proposes to consider the possibility of participating in the concept of demand management of educational institutions with a typical workload schedule of the work week. For the study, statistical data of open services and sources, Russian and foreign research on the use of digital and information technologies, analytical methods, methods of mathematical modeling, methods of analysis, and generalization of data and statistical methods of data processing are used. An algorithm for collecting and processing power consumption data and a load planning algorithm were developed, including all levels of interaction between devices. A comparison was made between the values of the maximum daily consumption before and after optimization, as well as the magnitude of the decrease in the maximum consumption after applying the genetic algorithm. The developed algorithm has the ability to scale, which will increase the effect of using the results of this study to more significant values. Load switching helps to reduce peak consumption charges, which often represent a significant portion of the electricity cost.

In this article actual topics concerned with mechanical properties of bulk materials, usage of computer vision and artificial neural networks in this research are discussed. The main principles of the system for analysis of bulk materials mechanical characteristics are described. Bulk material outflow behaviour with predefined parameters (particles shapes and radius, coefficients of friction, etc.) was modelled. The outflow was modelled from the calibrated conical funnel. Obtained dependencies between mechanical characteristics and pile geometrical properties are represented as diagrams and graphs.

Based on the literature review, the authors arrive at the conclusion that abrasivity of rocks is not only governed by their strength, but also by the structure and parameters of mineral inclusions, such as shape, size and amount. In terms of the Khibiny’s apatite–nepheline ore of various kinds with different strength and mineralogical composition, it has experimentally been estimated using the modified Baron–Kuznetsov procedure how the strength of rocks affects the wear rate of specimens made of steel 110G13L that is most often used for manufacturing rapidly wearing parts of mining equipment. It is found that for all ore kinds tested, the steel wear rates are approximately the same irrespective of the ore strength. It has been supposed that on abrasion of steel on hard rock (f ~ 10) composed of grains of hard tabular nepheline with thin partings of relatively soft fine-grain apatite, nepheline grains disengage slowly, the contact surface becomes smooth and steel wear rate is reduced consequently. On the other hand, on abrasion on weak rock (f ~ 0.5) composed mostly of apatite, fast failure of this type rock causes continuous exposure of rough-surface apatite grains, which accelerates steel wear resistance. On stee