The article examines methods for assessing the efficiency of electric mining excavators, emphasizing the inseparability of operational efficiency from the operator-machine ergatic system. It reviews methods for evaluating operator skills via experimental data and proposes a comprehensive approach to assess the excavator's operational efficiency and the operator's skill level. This method includes analyzing the machine's operating time and energy efficiency using a simulator, thereby offering a novel perspective on the dynamic interaction between human operators and automated systems. With the working cycle's duration measured by the ratio of average to nominal cycle times, and energy efficiency assessed through the comparison of specific energy consumption to theoretical values. The findings suggest prioritizing reductions in operating cycle time for suboptimal machine control and focusing on improving bucket fill rates to enhance energy efficiency. Moreover, the study underscores the potential for utilizing these methodologies in real-world applications, aiming to optimize the utilization of mining equipment and thereby significantly contribute to the advancement of operational methodologies in the mining sector.

The 21st century is characterized not only by large-scale transformations but also by the speed with which they occur. Transformations—political, economic, social, technological, environmental, and legal-in synergy have always been a catalyst for reactions in society. The field of energy supply, like many others, is extremely susceptible to the external influence of such factors. To a large extent, this applies to remote (especially from the position of energy supply) regions. The authors outline an approach to justifying the development of the Arctic energy infrastructure through an analysis of the demand for the amount of energy consumed and energy sources, taking into account global trends. The methodology is based on scenario modeling of technological demand. It is based on a study of the specific needs of consumers, available technologies, and identified risks. The paper proposes development scenarios and presents a model that takes them into account. Modeling results show that in all scenarios, up to 50% of the energy balance in 2035 will take gas, but the role of carbon-free energy sources will increase. The mathematical model allowed forecasting the demand for energy types by certain types of consumers, which makes it possible to determine the vector of development and stimulation of certain types of resources for energy production in the Arctic. The model enables considering not only the growth but also the decline in demand for certain types of consumers under different scenarios. In addition, authors’ forecasts, through further modernization of the energy sector in the Arctic region, can contribute to the creation of prerequisites that will be stimulating and profitable for the growth of investment in sustainable energy sources to supply consumers. The scientific significance of the work lies in the application of a consistent hybrid modeling approach to forecasting demand for energy resources in the Arctic region. The results of the study are useful in drafting a scenario of regional development, taking into account the Sustainable Development Goals, as well as identifying areas of technology and energy infrastructure stimulation.

Any technological process including metallurgical processes is supported by the control system operation, accompanied by large information flows to be formed. However, the most part of this information is not used by specialists due to restricted capabilities of a human being that is, in fact, incapable of processing such information flows. It has been demonstrated that process control is significantly influenced by the human factor. This paper contains a methodology to process production information that permits the process personnel to use its potential in a more rational way to control the process. An approach has been reviewed to studying metallurgical processes using the analysis of indirect indicators of process management, namely the spectral density and auto-correlation function of main process parameters. A method to separate useful signals from noise has been studied. A method has been given to check the ACS management efficiency using primary material flows. The adopted methodology for processing experimental data permits interpreting the obtained results for their further practical application to develop new algorithms for process control and improve the existing control system.