The article discusses the extraction of solid mineral resources (SMR) from the bottom, such as ferromanganese nodules (FMN) and cobalt-manganese crusts (CMC), occurring in both offshore and deep regions of the World Ocean. For separating and lifting the water surface is used mining complex, which includes a hydraulically dragheads and intermediate capsule with atmospheric pressure introduced into the of extraction process in order to increase energy efficiency through the organization of a two-stage hydroascent, the first stage is carried out by external hydrostatic pressure. To determine the optimum depth of immersion of the intermediate capsule composed mathematical model. It is based on the sustainable hydroascent provided at the first stage transport (from the bottom to capsule), and reducing power consumption slurry pump performing the second stage transport (from the capsule to the sea surface). In determining the rational immersion depth were taken into account design features of slurry pipeline, mine geological parameters, the parameters of extracted minerals, performance of dragheads, the slurry flow parameters, such as concentration, consistency, density. Presents an example calculation and determined the optimal capsule insertion depth for offshore geological environments.

Nowadays pelletizing drums are widely used in the steel industry. These units are characterized by high endurance and low cost of maintenance. However, use and control of these units in the process of coarsening have a number of issues. For most of the cases pelletizing drums are “black box” and control accuracy can not be estimated exactly. It is explained by low existing theoretical basis of this production process. Particularly it is tied up with the variability of the bulk materials (charges) parameters supplied to the unit. Overcome of this issues can be reached with development of intelligent control systems for drum pelletizing machines. Main requirement for such systems is possibility to level or consider the effect of charges properties variability in control. However, it is necessary to study the behavior of bulk materials inside the units. Visual assessment of pelletization does not allow to evaluate the ongoing physical processes. Development of mathematical and numerical models can help studying the process and take a lot of parameters into account including charges properties and even interaction with water. But the adequacy of the resulting models also has to be clarified using physical devices to record or capture bulk materials behavior inside the units. This research proposes a DEM simulation test of the concept for bulk material behavior control through the recognition of the mixture movement fragments using special capsules. This part is dedicated to the simulation model set up and extracting the particles trajectories for further processing.

In this paper, we consider the need to create an algorithmized digital platform for the development of electric charging networks of road transport and to optimize the load on the current power grid complex. In modern conditions, this platform can become an example of a mechanism for public-private partnerships and the development of environmentally friendly transport. The purpose of the platform is to provide users with up-to-date information about the state of the electric vehicle battery, reduce the cost of creating a network charging infrastructure for electric vehicle transport, automated resource accounting for power supply companies, and other services. The use of information technologies to combine the processes of economic relations in the field of energy sales services will attract funds for infrastructure development by increasing the load factor of generating capacities and unloading power centers. In the article, the authors propose a digital platform architecture using modern technologies such as IIoT, Vehicle to Grid (V2G), Machine learning, Big Data.