Big data analysis is becoming a daily task for companies all over the world as well as for Russian companies. With advances in technology and reduced storage costs, companies today can collect and store large amounts of heterogeneous data. The important step of extracting knowledge and value from such data is a challenge that will ultimately be faced by all companies seeking to maintain their competitiveness and place in the market. An approach to the study of metallurgical processes using the analysis of a large array of operational control data is considered. Using the example of steel rolling production, the development of a predictive model based on processing a large array of operational control data is considered. The aim of the work is to develop a predictive model of rolling mill roll wear based on a large array of operational control data containing information about the time of filling and unloading of rolls, rolled assortment, roll material, and time during which the roll is in operation. Preliminary preparation of data for modeling was carried out, which includes the removal of outliers, uncharacteristic and random measurement results (misses), as well as data gaps. Correlation analysis of the data showed that the dimensions and grades of rolled steel sheets, as well as the material from which the rolls are made, have the greatest influence on the wear of rolling mill rolls. Based on the processing of a large array of operational control data, various predictive models of the technological process were designed. The adequacy of the models was assessed by the value of the mean square error (MSE), the coefficient of determination (R²), and the value of the Pearson correlation coefficient (R) between the calculated and experimental values of the mill roll wear. In addition, the adequacy of the models was assessed by the symmetry of the values predicted by the model relative to the straight line Ypredicted = Yactual. Linear models constructed using the least squares method and cross-validation turned out to be inadequate (the coefficient of determination R² does not exceed 0.3) to the research object. The following regressions were built on the basis of the same operational control database: Linear Regression multivariate, Lasso multivariate, Ridge multivariate, and ElasticNet multivariate. However, these models also turned out to be inadequate to the object of the research. Testing these models for symmetry showed that, in all cases, there is an underestimation of the predicted values. Models using algorithm composition have also been built. The methods of random forest and gradient boosting are considered. Both methods were found to be adequate for the object of the research (for the random forest model, the coefficient of determination is R² = 0.798; for the gradient boosting model, the coefficient of determination is R² = 0.847). However, the gradient boosting algorithm is recognized as preferable thanks to its high accuracy compared with the random forest algorithm. Control data for symmetry in reference to the straight line Ypredicted = Yactual showed that, in the case of developing the random forest model, there is a tendency to underestimate the predicted values (the calculated values are located below the straight line). In the case of developing a gradient boosting model, the predicted values are located symmetrically regarding the straight line Ypredicted = Yactual. Therefore, the gradient boosting model is preferred. The predictive model of mill roll wear will allow rational use of rolls in terms of minimizing overall roll wear. Thus, the proposed model will make it possible to redistribute the existing work rolls between the stands in order to reduce the total wear of the rolls.

Radial charts were commonly used in the industry to allow retrospective assessment of technological parameters. Today it is relevant to digitize the obtained data in order to simplify the automation of technological processes. Digitization of radial charts by means of standard methods is carried out with the help of human labor at significant time costs. The article proposes an automated method for digitizing radial charts using software, developed in the LabVIEW programming environment. The results of processing radial charts are displayed on the screen in numerical and graphical form, and can also be exported to a file (for example, to Notepad or MS Excel). The presented technique can be applied to images obtained on a color or black-and-white scanner, which minimizes geometric distortions, associated with the conversion of a paper document into electronic form, and ensures recognition quality of the clear plot line with an average relative error of up to 3 %. In case of ink fading or perspective photos of the diagram, the value of relative error can reach 8 %, as a result of which additional manual correction of the data will be required.

Copper-sheathed steel wires combine the conductivity of copper and the traction resistance of steel, which makes a bimetallic wire an ideal material for the construction of power lines. Currently, there is a small number of studies devoted to the change in the microstructure of steel-copper wire during its strain. Since steel and copper have different mechanical properties, these metals at the interface can be deformed in different ways. Therefore, the present research is devoted to the study of ECAP-drawing process impacts on the properties of bimetallic steel-copper wire. During the conducted studies, the possibility and efficiency of using the combined strain technology for the formation of ultrafine grained structure and increased strength properties of steel-copper wire have been proved.

This article presents the results of the possible utilization of technogenic raw materials, in particular, technogenic tailings of the Nadezhdinsky Metallurgical Plant, slags of copper production of the mining and metallurgical plant Norilsk Nickel by processing them as secondary mineral raw materials in order to reduce their anthropogenic impact on the environment of the Arctic region. In particular, studies were conducted to optimize the composition of the raw material mix and chemical and mineralogical composition of cement clinker. optimization was carried out using the software package “ROCS”, designed for the calculation and optimization of cement raw mixes depending on the saturation coefficient, with determining the component chemical composition of the raw mix and cement clinker, mineralogical composition of cement clinker, the thermal effect of clinker formation (FEC) and fuel consumption for firing (Gfuel). In the course of the research it was found that: technogenic raw materials in the form of tailings and slags of copper production can be used as secondary mineral raw materials for cement clinker production; depending on the saturation coefficient, it is possible to obtain cement clinker of a certain mineralogical composition, both low - base (belite) and high-base (alite); depending on the mineralogical composition of clinker, optimal compositions of raw mixtures for belite (low-base) cement clinker-limestone are as follows: 72.97%; dump tails-0.71%; slag-26.32%, and for alite (high - base) - limestone-81.71%; dump tailings-3.52%;...

Over the last few decades, the demand for energy-efficient mineral-processing methods has continued. The necessity to develop energy-efficient technologies for the mineral industry will increase in the future, considering the exhaustion of high-quality resources and severe environmental limitations. The subject of this study is crushing equipment. It is a complex of units designed to reduce the fraction of ore and non-metallic solid materials. It is also designed to make them more symmetrical in order to facilitate their transport and later use in production. Thus, the urgency of using crushers in mining and processing plants is clear, so it is relevant to find ways to optimize their operation and reduce energy consumption. This article presents a systematic review of the task of improving the energy efficiency of crushing units. This is achieved by studying modelling methods and results, the automation of crushing and grinding processes, and the wear reduction of crusher components. On the grounds of the reviewed sources, the main methods of increasing the efficiency of crushing units are identified. A mathematical model of the cone crusher was designed. The simulation error is less than 6%. A simulation experiment was carried out on the mathematical model. The dependences of the current and power of the crusher electric drive on the feeder capacity are determined; the graphs have a symmetrical position relative to the approximating curve (R² ≈ 0.9).

The relevance of the research is due to the need to stabilize the composition of the melting products of copper-nickel sulfide raw materials in the Vanyukov furnace. The goal of this research is to identify the most suitable methods for the aggregation of the real time data for the development of a mathematical model for control of the technological process of melting copper-nickel sulfide raw materials in the Vanyukov furnace. Statistical methods of analyzing the historical data of the real technological object and the correlation analysis of process parameters are described. Factors that exert the greatest influence on the main output parameter (copper content in matte) and ensure the physical-chemical transformations are revealed. An approach to the processing of the real time data for the development of a mathematical model for control of the melting process is proposed. The stages of processing the real time information are considered. The adopted methodology for the aggregation of data suitable for the development of a control model for the technological process of melting copper-nickel sulfide raw materials in the Vanyukov furnace allows us to interpret the obtained results for their further practical application.