The article describes the current situation in the global uranium concentrate market, explains the reasons for the formation of an excess amount of finished goods and the subsequent decreasein prices. The authorsevaluated the prospects of using market mechanisms to improve the financial results of Russian uranium mining enterprises. The location of the main mining centers in comparison with the centers of consumption of finished goods, pricing for the products of uranium mining companies, the dynamics of price changes over the past 20 years, the influence of non-market factors on the supply of finished products are analyzed.This study led to the conclusion that the expectation of changes in the market situation is not viable in a long term. The authors outlined the direction to improve the profitability of mining enterprises by introducing technological changes aimed at reducing the cost of the final product.

This paper presents a numerical simulation of powder sintering. The numerical model presented in this paper uses the discrete element method, which suggests that the material can be modeled by a large set of discrete elements (particles) of a spherical shape that interact with each other. A methodology has been developed to determine the DEM parameters of bulk materials based on machine vision and a neural network algorithm. The approach is suitable for obtaining the exact values of the DEM parameters of the investigated bulk material by comparing the visual images of the material’s behavior at the experimental stand in reality and in the model. Simulation of sintering requires an introduction of cohesive interaction between particles representing interparticle sintering forces. Numerical sintering studies were supplemented with experimental studies that provided data for calibration and model validation. The experimental results have shown a significant capability of the designed numerical model in modeling sintering processes. Evolution of microstructure and density during sintering have been studied under the laboratory conditions.

Big Data analysis is becoming an everyday task for companies all over the world, including Russian companies. Due to advances in technology and the reduction in the cost of storage systems, companies can now collect and store large volumes of heterogeneous data. The important step of extracting knowledge and value from such data is a challenge that will eventually be met by all companies seeking to maintain their competitiveness and place in the market. However, companies face several challenges when it comes to collecting, pre-processing, and integrating data into cohesive data sets designed to deliver analytics. In this article, the above problems and possible solutions are illustrated using the example of cleaning, integration, and normalization of data obtained in the measurement of indicators for the Vanyukov melting furnace process. The article considers an approach to the study of metallurgical processes using the analysis of large operational control data sets. Standard methods of processing the data sets of operational process control are used. The correlation analysis of the main process parameters is carried out. The results are interpreted for their further practical application.