G. A. Gareeva (KNITU-KAI, Naberezhnye Chelny); R. R. Basyrov (Naberezhnye Chelny Institute of KFU, Naberezhnye Chelny); I. I. Frolova (V. G. Timiryasov State University of Economics, Naberezhnye Chelny); I. I. Mubarakshina (NСSPU, Naberezhnye Chelny); A. F. Ziyatdinov (TISBI University of Management, Naberezhnye Chelny Branch, Naberezhnye Chelny)

Automated information system for monitoring the life cycle of technical equipment and software at the enterprise

The article presents the process of developing an automated information system for monitoring the life cycle of technical equipment and software at the enterprise, i. e. creating a convenient and functional tool that will help optimize business processes at the enterprise and improve the efficiency of resource management.
Keywords: information systems; life cycle management; technical equipment; software; SQLite; Neovim; Python; Django.

 

I. H. Utakaeva (Financial University under the Government of the Russian Federation, Moscow)

Optimization of raw material logistics routes using a combination of graph theory and hybrid AI algorithms

Modern logistics systems operate in conditions of increasing complexity and uncertainty, which requires the development of innovative methods for optimizing raw material transportation routes. This study aims to create an integrated approach to optimizing logistics routes based on the synthesis of graph theory and hybrid algorithms of artificial intelligence. The paper presents a multi-criteria optimization model that takes into account time, cost and environmental parameters when planning transport flows. The methodological basis of the study is a combination of shortest path search algorithms with adaptive neural network structures and genetic algorithms. The empirical part of the work is based on the analysis of data from 37 industrial enterprises covering various sectors of the manufacturing sector. The results of the study demonstrate that the use of the developed model allows to reduce logistics costs by an average of 22,7 %, delivery time by 18,3 %, and the carbon footprint by 15,1 % compared to traditional routing methods. The conducted cluster analysis revealed high adaptability of the model to seasonal fluctuations in demand with an elasticity coefficient of 0.83. Of particular value is the integrated algorithm for balancing the load of the logistics network, which showed a 31,4 % increase in productivity under peak loads. The proposed approach has significant potential for practical implementation in supply chain management systems and opens up prospects for further research in the field of self-learning systems for logistics optimization and predictive analytics of freight flows.
Keywords: graph theory; hybrid algorithms; route optimization; logistics systems; artificial intelligence; supply chains; multicriteria modeling.

 

S. A. Tronin (Financial University under the Government of the Russian Federation, Moscow)

Development and implementation of digital platforms for managing production operations and supply chains in industrial enterprises

The article is devoted to the study of the processes of development and implementation of digital platforms for managing production operations and supply chains in industrial enterprises. The key factors influencing the effectiveness of the implementation of such platforms are identified, and practical recommendations for optimizing these processes are developed. Four groups of factors are found that largely determine the success of the implementation of digital platforms: technological, organizational, personnel and institutional. It has been established that the key role is played by the quality of integration of platforms with existing IT systems, the involvement of top management, digital competencies of personnel and support from industry associations. The results obtained are significant for the development of the theory of digital transformation of industry and can be used in developing strategies for the digitalization of enterprises. In the future, it is advisable to expand the industry and geographic scope of the study, as well as to deepen the analysis of the relationships between the identified factors.
Keywords: digital platforms; industrial enterprises; supply chains; production operations; implementation factors; empirical analysis.

 

Sun Zhaoyang, Xu Luhan (Don State Technical University, Rostov-on-Don)

Application of machine learning methods for developing innovative logistic solutions in Russian-Chinese transport corridors

This article explores the potential of applying machine learning (ML) methods to optimize logistics processes within the framework of Russian-Chinese transport corridors. The study substantiates the relevance of cross-border logistics digitalization amid increasing foreign trade turnover and the growing complexity of infrastructure connectivity between the two countries. It provides an overview of key ML algorithms (regression, clustering, and reinforcement learning) with a focus on their practical application in forecasting, routing, and logistics resource management. The article presents examples of successful implementation of intelligent systems in border regions and offers practical recommendations for the integration of ML technologies into the existing transport infrastructure. Particular attention is paid to digital compatibility issues, the role of public-private partnerships, and the need to establish a unified information environment. The findings confirm the effectiveness of ML as a tool for enhancing the resilience and adaptability of logistics systems along the Eurasian transport axis.
Keywords: machine learning; cross-border logistics; transport corridors; logistics digitalization; intelligent systems; multimodal transportation.

 

I. H. Utakaeva (Financial University under the Government of the Russian Federation, Moscow)

Optimizing predictive equipment maintenance by combining industrial IoT data and graph knowledge bases

This study aims to develop an integrated approach to predictive maintenance of industrial equipment through the convergence of the Industrial Internet of Things (IIoT) and graph knowledge bases (KG). The integration of these technologies creates significant potential for overcoming the limitations of traditional maintenance methods, which often suffer from data siloing, limited contextualization, and weak semantic interpretation of the collected information. The study proposes a multi-level architecture that includes data collection from IIoT devices, their pre-processing, metadata generation, construction of a semantic model of the subject area in the form of a graph knowledge base, and the use of machine learning algorithms for predictive analytics. The empirical verification of the developed approach was carried out on three types of production equipment (hydraulic systems, conveyor lines, robotic complexes) for 18 months. The results show a significant increase in the accuracy of failure prediction (by 27,4 % compared to traditional methods), a reduction in unplanned downtime by 31,8 %, and a decrease in total maintenance costs by 24,2 %. The developed framework demonstrates increased adaptability to changes in operating conditions due to dynamic updating of the knowledge graph and enrichment of the data context, which provides a deeper understanding of the cause-and-effect relationships between various factors affecting the condition of the equipment. The study opens up new prospects for the creation of intelligent systems for servicing industrial assets in the Industry 4.0 paradigm.
Keywords: predictive maintenance; industrial internet of things; graph knowledge bases; machine learning; semantic data integration; intelligent manufacturing; cyber-physical systems.

 

Yu. A. Krupnov (Financial University under the Government of the Russian Federation, Moscow); O. V. Fokina (Vyatka State University, Kirov) 

Corporate strategies of metallurgical enterprises in the innovative economy

The purpose of the study is to substantiate and select the corporate strategy of enterprises in the metallurgical production market in an innovative economy. As a hypothesis, the assumption is made about the applicability of strategies formulated by specialists of the Boston Consulting Group. Linking the proposed strategies to indicators of the level of innovative development of the industry and the level of competition, as well as an analysis of the development of the metallurgical production market, made it possible to determine the type of corporate strategy of manufacturers, identify key success factors of the strategy and possible dangers of its implementation. 
Keywords: metallurgical production; innovative economy; corporate strategy; success factors; competition.

 

I. Yu. Vaslavskaya (Kazan (Volga Region) Federal University, Kazan); Ya. I. Vaslavskiy (Moscow State Institute of International Relations (MGIMO University), Moscow); R. M. Magomedov, S. V. Savina, E. P. Kochetkov (Financial University under the Government of the Russian Federation, Moscow)

Analysis of the main factors and mechanisms for ensuring sustainable competitive advantages of industrial enterprises

The article analyzes the main factors affecting the competitiveness of industrial enterprises in a rapidly changing economy. The article analyzes the importance of innovation, operational efficiency and strategic positioning in creating sustainable competitive advantages. It is determined that successful adaptation to modern conditions requires industrial enterprises to develop comprehensive strategies covering not only internal processes, but also external cooperation and political support. The dynamic interaction between various factors that shape the competitive environment is noted and emphasizes the importance of continuous innovative development and strategic flexibility to ensure the long-term competitiveness of industrial enterprises.
Keywords: competitiveness; industrial enterprises; competitive advantages; management.

 

N. V. Meller, I. Yu. Nekrasova (Tyumen Industrial University, Tyumen)

Timing research as a tool for optimizing time standards at a machine-building enterprise

The article examines the problem of discrepancies between standard and actual time expenditures for performing operations regulated by the operational process chart (OPC) at a machine-building enterprise. The article substantiates the need to revise existing time standards in order to improve the efficiency of production processes when performing operations described in the OPC. A comprehensive approach to standardization optimization is proposed, based on conducting timing studies and subsequent development of practical recommendations for revising and approving new, more realistic time standards for operations performed in accordance with the OPC. The universality of the approach lies in the ability to adapt to the specifics of a particular machine-building enterprise and the features of technological processes. The implementation of the proposed approach will allow enterprises to increase the efficiency of labor resources and improve product quality.
Keywords: labor standards; timekeeping; mechanical engineering; operational technological map (OTC); time standards; optimization of production processes; labor costs; production efficiency.

 

An. B. Mottaeva (Institute of Management Research and Consulting, Faculty of Higher School of Economics, Financial University under the Government of the Russian Federation, Moscow)

Building a model for Russian small and medium-sized enterprises to enter foreign markets

The development of SMEs is an important component of the Russian economy. In the context of globalization and the sanctions regime, the entry of Russian SMEs into foreign markets is one of the key elements for the development of not only the enterprises themselves, but also the entire national economy. Russian SMEs faced new challenges, which predetermined the need to find new ways and methods of building a model for entering foreign markets. The relevance of the topic necessitated the development of an algorithm for marketing strategy and an algorithm for entering foreign markets for SMEs based on the analysis of external and internal indicators. The purpose of the study is to develop a clear and specific algorithm for SMEs to enter foreign markets.
Keywords: small and medium-sized businesses; export; foreign market; small business problems; development of new foreign markets; export potential; entrepreneurship.

 

O. B. Skripnik (Financial University under the Government of the Russian Federation, Moscow)

Methodology for assessing economic risks in the implementation of intelligent data analysis systems in industrial production

The rapid development of artificial intelligence technologies and analytical systems is transforming the industrial sector, creating new opportunities for optimizing production processes while simultaneously creating specific economic risks. This study is aimed at developing a comprehensive methodology for assessing and managing economic risks arising from the integration of intelligent data analysis systems into industrial production. The methodological base of the study includes system analysis, economic and mathematical modeling, multi-criteria optimization methods and a Bayesian approach to assessing the probabilities of risk events. The empirical basis was data from 78 industrial enterprises of various industries that implemented analytical systems in the period 2018–2023. As a result of the study, a risk identification matrix was developed, including 27 specific factors with corresponding weighting coefficients, a parametric model for predicting economic losses with an accuracy of 87,3 % was proposed, and a three-level system of implementation performance indicators was formed. The developed tools can reduce the uncertainty of investment decisions by 42,7 % and optimize the total cost of ownership of analytical systems by 18,5 %. The theoretical significance of the study lies in the formation of a conceptual approach to the stratification of digital transformation risks, and the practical significance lies in the development of algorithms for making management decisions that minimize economic losses while maintaining the innovative potential of enterprises.
Keywords: intelligent systems; economic risks; industrial analytics; digital transformation; investment efficiency; predictive modeling; risk management.

 

I. A. Rozhdestvenskaya, A. I. Kabalinskii (Financial University under the Government of the Russian Federation, Moscow)

Regional economic policy of ensuring technological sovereignty in the field of manufacturing industry development

The article analyzes the activity of authorities at all levels and businesses in developing activities related to manufacturing in Russian regions, both traditionally focused on these segments of the industry and promising in terms of creating new industries in the context of minimizing dependence on imports and ensuring technological sovereignty. 
Keywords: industry; manufacturing; regions; industrial policy; new technologies; import substitution.

 

S. G. Sternik (Institute of Economic Forecasting, RAS; Financial University under the Government of the Russian Federation; Moscow University «Synergy», Moscow); A. A. Pomulev (Financial University under the Government of the Russian Federation, Moscow)

Selection of external factors and methods for forecasting the dynamics of industrial production by analogy with modeling state budget revenues using artificial intelligence algorithms

Purpose: is substantiation of methods for selecting indicators for the development of a model for forecasting state budget revenues and dynamics of industrial production using the section of artificial intelligence – machine learning. The empirical base of the study consists of official data from Rosstat, ministries and departments of Russia for the period from 2013 to 2023. Results: an approach to the selection of predictor indicators based on the use of various machine learning methods is proposed, their comparative analysis is given, their advantages and disadvantages are revealed. Practical recommendations are given for the application of machine learning methods and the selection of predictor indicators for forecasting the dynamics of industrial production by analogy with modeling state budget revenues. Based on the results of testing statistical methods for selecting the significance of indicators for the development of a predictive model, it was found that these methods are characterized by subjectivity when choosing a threshold for cutting off the degree of significance of an indicator. Twelve linear and twelve machine learning models with different methods for selecting indicators have been built. As a result, it was found that the best results are demonstrated by random forest models with RFE and CFS selection methods with an average forecast accuracy above 90 %. The article can be useful to public authorities and practitioners in the field of public finance. 
Keywords: revenues of the federal budget; industrial sector; forecasting; predictor indicators; machine learning methods; artificial intelligence.

 

O. B. Skripnik (Financial University under the Government of the Russian Federation, Moscow)

Analysis of the impact of Big Data on risk management strategies in industrial investment projects in the context of digital transformation

The intensive implementation of big data technologies in industrial production is transforming traditional approaches to investment risk management. This study is aimed at a systematic analysis of the mechanisms of big data influence on the processes of risk identification, assessment and minimization in investment projects of the industrial sector. The methodological basis of the study includes a comprehensive multi-level analysis of data collected from 317 industrial enterprises in Russia for the period 2019–2023 that implemented investment projects with the integration of Big Data technologies. The methods of correlation, factor and regression analysis were used, supplemented by expert assessments and in-depth interviews with top management representatives. The results of the study demonstrate that the integration of big data analytics into risk management systems increases the predictive accuracy of risk assessment by 27,4 %, reduces the response time to risk events by 41,6 % and reduces financial losses from risk realization by an average of 32,8 %. A multi-component model of predictive risk analytics has been developed, including machine learning algorithms, demonstrating 89,7 % efficiency when verified on historical data. The results obtained expand the theoretical basis of risk management in the digital economy and provide practical tools for optimizing investment strategies in industrial production.
Keywords: big data; risk management; investment projects; industrial production; digital transformation; predictive analytics; technological integration.

 

I. Yu. Vaslavskaya, D. M. Lysanov (Naberezhnye Chelny Institute the branch of Kazan Federal University, Naberezhnye Chelny)

Methodology of complex assessment of efficiency of functioning of car service enterprises

The article is devoted to the development of a system of indicators for the comprehensive assessment of enterprises' activities, oriented to market conditions and taking into account both external and internal factors of functioning. The main attention is paid to the selection and justification of efficiency criteria, among which the key one is the minimum present value of costs, taking into account the costs of maintenance and repair of vehicles.
Keywords: enterprise; maintenance; repair; costs; factors; criterion; efficiency; management.

 

N. E. Lebedeva (Financial University under the Government of the Russian Federation, Moscow); K. B. Kunanbayeva (Toraighyrov University, Pavlodar, Republic of Kazakhstan)

Strategic management of the process of overcoming technological dependence in the oil and gas sector

The article considers strategic approaches to managing the process of overcoming technological dependence in the Russian oil and gas sector under sanctions. The study uses content analysis of existing domestic and foreign sources, applies statistical data and comparative analysis. The article proposes solutions to the problems of the oil and gas sector, including modernization of tender procedures, development of laboratory and testing infrastructure, interdepartmental cooperation and standardization, as well as financial and organizational support. It is concluded that the implementation of the measures proposed by the authors will reduce technological dependence, increase technological independence and strengthen Russia's position in the international energy market.
Keywords: oil and gas sector; sanctions; technological dependence; innovations; export; tender procedures; laboratory infrastructure; financial support; strategic management; economic sustainability.

 

S. G. Eremin (Financial University under the Government of the Russian Federation, Moscow); O. V. Arashkevich (Francisk Skоryna Gomel State University, Gomel, Republic of Belarus)

Study of financial instruments of industrial policy and their impact on economic growth

The article is devoted to the analysis of the impact of financial instruments of industrial policy on economic growth. The purpose of the study is to identify the key financial instruments of industrial policy and assess their impact on economic dynamics. Four clusters of countries by type of industrial policy were identified. It is proven that targeted concessional loans, R & D grants, fiscal incentives significantly increase industrial growth, and the effect is enhanced in countries with better institutions. The contribution to GDP reaches 1.5–2 % over a 5-year horizon. The results are valuable for substantiating industrial policy, taking into account country specifics. Prospects are related to the analysis of the impact of instruments on technological innovation.
Keywords: industrial policy; financial instruments; economic growth; panel analysis; preferential loans; grants; fiscal incentives.

 

M. A. Danilkevich (Financial University under the Government of the Russian Federation, Moscow)

State of development of additive technologies in the Russian Federation in 2025

Additive manufacturing technologies are currently used in many areas of production. The development of such production can help bring industrial production in Russia to a higher level. State support is one of the important tools for the development of additive manufacturing. This article examines the current state, achievements and main problems of the development of additive technologies in the Russian Federation. Data on the level of production localization, popular technologies, application areas and development prospects are provided. Special attention is paid to the role of state support and infrastructure limitations.
Keywords: scientific and technological development; additive technologies; additive manufacturing; government support; three-dimensional printing.

 

N. L. Krasyukova (Financial University under the Government of the Russian Federation, Moscow)

National characteristics of industrial development

The article is devoted to the analysis of the application of state management instruments for industrial development in different countries of the world. Common industrial policy instruments for most countries are identified: the application of long-term planning and reliance on innovation.
Keywords: instruments of public administration; industrial policy; planning; innovations

 

N. V. Meller, I. Yu. Nekrasova (Tyumen Industrial University, Tyumen)

Analysis of the wage system at a mechanical engineering enterprise: problems and ways of improvement

The article considers the problem of wages not matching the labor market and the lack of consideration of the labor participation coefficient (LPC) at a mechanical engineering enterprise. The current wage system based on the tariff rate, time worked and bonus proportionally distributed among the team members is analyzed. The shortcomings of this system leading to a decrease in employee motivation and an outflow of qualified personnel are revealed. Ways to improve the wage system are proposed, including consideration of market conditions, the introduction of the LPC and a revision of the bonus principles.
Keywords: wages; labor participation coefficient (LPC); mechanical engineering; labor motivation; tariff rate; bonus; labor market; efficiency.

 

A. P. Tsypin (Financial University under the Government of the Russian Federation, Moscow) 

Retrospective analysis of the development of the Russian metallurgical industry

Advanced metallurgy is a kind of indicator of the success of both the manufacturing industry and the entire economy of the country. In addition to making a significant contribution to the consolidated budget, the metallurgical industry creates a significant number of jobs, gives impetus to the development of related economic activities, and also forms the technological security of the country. Taking into account the above facts, it is possible to formulate the purpose of the research, which is to analyze the historical path of development of Russian metallurgy through the prism of statistical science. At the same time, such mathematical and statistical methods of scientific knowledge as tabular, graphical, and time series analysis were used. The main results of the study include the following: based on statistical material, three major periods of development can be distinguished, which in turn are divided into subperiods, each of which is characterized by its own characteristics; the first stage dates back to the time of the Russian Empire and is characterized by a gradual increase in the production of cast iron and steel; the second stage covers the period 1917–1990. It is characterized by the formation of a scientific cluster dealing with metallurgy problems and the construction of large factories; the third stage begins in 1991 and continues to the present and is characterized by a sharp decline in production in the 1990s and a steady growth in the period 2009–2022. Thus, the Russian metallurgical industry has gone through a long development path and currently occupies a leading position in the global metal production and export space.
Keywords: metallurgy; historical development path; dynamics; statistical indicators; key indicators.

 

O. V. Panina (Financial University under the Government of the Russian Federation, Moscow

Industrial management in the period of industrialization: foreign aid, the first results and the beginning of a new management reform (part 1)

The approaches to industrial management in the context of industrialization are analyzed. The author substantiates the thesis that at that time there was another active search for new forms, methods and tools of industrial management in the context of the transition of all industrial enterprises to state ownership. The foreign aid received at the first stage and its rather rapid reduction in conditions when domestic industrial enterprises were able to provide import substitution of basic industrial products and the first results of industrialization required the search for new forms of industrial management.
Keywords: Ministry of Trade and Industry; industry; public administration; industrial complex; industrial policy; modernization; industrialization.

 

Sh. A. Osmanov, Yu. N. Belshina, A. P. Korolkov, S. A. Pogrebov (Saint-Petersburg State Fire Service University of EMERCOM of Russia, Saint Petersburg)

The use of robotics to eliminate the consequences of oil spill

The paper examines modern achievements in the field of robotic systems used for monitoring, localization and cleaning of oil spills in the aquatic and terrestrial environment. Key technologies are analyzed, including autonomous underwater and surface drones, robotic manipulators, bioinspired robots and artificial intelligence systems that increase the effectiveness of liquidation measures. Special attention is paid to the prospects for the development of robotics in this area, including integration with neural networks, the use of new sorption materials and the introduction of coordinated swarm technologies. 
Keywords: robotics; oil spills; autonomous drones; bioinspired robots; artificial intelligence; sorption materials; swarm technologies.

 

P. I. Chuvakhin (Financial University under the Government of the Russian Federation, Moscow)

The role of big data in minimizing environmental and financial risks in the implementation of infrastructure projects in the manufacturing industries

In the context of the increasing complexity of infrastructure projects in the manufacturing industries, the problem of minimizing the associated environmental and financial risks is becoming particularly relevant. Modern technologies for processing and analyzing big data open up fundamentally new opportunities for modeling, forecasting and preventing potential threats. This study is aimed at developing a comprehensive methodology for integrating big data tools into the risk management system when implementing infrastructure projects. The methodology is based on a systems approach that combines quantitative and qualitative research methods, including time series analysis, multivariate statistical modeling, machine learning and expert assessments. The empirical base consisted of data on 134 major infrastructure projects implemented in the metallurgical, chemical and mechanical engineering industries in the period 2018–2023. The study found that the use of predictive analytics based on big data can increase the accuracy of identifying environmental risks by 37,8 % and reduce the financial loss ratio by 29,4 %. The developed models demonstrated the ability to identify hidden patterns in accumulated data arrays with a forecasting accuracy of up to 83,7 %. Key factors for the effective integration of big data technologies into the risk management system were identified, including optimization of information architecture, standardization of data collection protocols, and the formation of relevant personnel competencies. The proposed methodology opens up prospects for the formation of a unified digital platform for managing environmental and financial risks, adapted to the specifics of manufacturing industries.
Keywords: big data; risk management; infrastructure projects; environmental risks; financial risks; predictive analytics; manufacturing industry.

 

I. A. Rozhdestvenskaya, S. G. Eremin, S. A. Zudenkova, O. A. Sagina (Financial University under the Government of the Russian Federation, Moscow)

Innovative ecosystem of the region – innovative infrastructure and three-level sources of innovative law

The article is devoted to the analysis of the innovative ecosystem of the region, its innovative infrastructure and three-level sources of innovative law. Innovative infrastructure is considered as one of the elements of the innovative ecosystem. The names of the stages of the innovation period are proposed. A study of theoretical approaches to the content of the innovative infrastructure and the possibilities of its transformation is conducted. The existing three-level system of sources of innovative law is studied: federal, regional and local levels; their relationship is studied. It has been established that in the future, further research is required aimed at determining the position of legal concepts in a single public legal field, as well as at the appropriate levels.
Keywords: innovation; region; digital transformation; sources of law; ecosystem.

 

M. M. Makhmudova (Industrial University of Tyumen», Tyumen)

Economic interest of business entities in environmental protection measures (using the example of enterprises in the Tyumen region)

The purpose of this study is to analyze the current dynamics and structure of financing labor protection measures by enterprises of the Tyumen region, as a reflection of their economic interest in creating and maintaining safe working conditions. Achieving this goal involves solving a number of tasks: analyzing the dynamics of statistical indicators of injuries at enterprises in the Tyumen region; identifying industry-specific injury patterns and employers' costs for preventive measures in the field of occupational safety; the structure of means of compensation to employers for labor protection costs from the SFR was described. As the analysis of the state of occupational injuries at enterprises in the Tyumen region has shown, the problem of reducing it is relevant today for business entities in various industries. Employers annually spend significant amounts of money on the implementation of preventive labor protection measures. A significant factor in the activation of labor protection activities by employers in the Tyumen region are instruments for financing preventive measures aimed at reducing occupational injuries and accidents.
Keywords: accident; occupational injury; social insurance; occupational safety; economic interest; Tyumen region.

 

Sh. A. Osmanov, A. P. Korolkov, D. E. Zavyalov, S. A. Pogrebov (Saint-Petersburg State Fire Service University of EMERCOM of Russia, Saint Petersburg)

Algorithm for supporting decision-making to determine the area of responsibility of rescue teams in the arctic zone of the Russian federation

A number of problematic issues of the Arctic zone of the Russian Federation, caused by its active development and intensification of cargo turnover carried by sea vessels, are being actualized. As one of the possible options for solving the revealed urgent problems, it is proposed to optimize the process of determining the zones of responsibility of rescue teams, taking into account the oceanographic and natural-climatic features of the region. For this purpose, a corresponding algorithm has been developed. The use of which is advisable both for determining the zones of responsibility of existing rescue teams and for determining new zones when deploying additional teams. As part of further research, it is planned to develop special software to automate this procedure. The approach used in the developed algorithm can be used in other subject areas, for example, when identifying the zones of responsibility of fire and rescue units and other units of the Ministry of Emergency Situations of Russia.
Keywords: algorithm; decision support; area of responsibility; rescue teams; Arctic zone of the Russian Federation; AZRF.

 

Shvartsburg L. E., Addas Safouh (MSUT «STANKIN», Moscow) 

Approaches to air purification in the 3D printing work area

This article discusses approaches to air purification in the working area of industries that use three-dimensional plastic layer-by-layer printing as part of their technological processes. The component composition of volatile organic compounds (VOCs) released into the working area of three-dimensional printing, their effect on the human body, and methods of air purification using modern technical means are considered. A simulation model of the VOC concentration distribution in the work area is proposed. Technical means of forced ventilation with VOC filtration are considered. The effectiveness of using multi-input cyclones for VOC separation in terms of size and deposition rate is shown. Based on the simulation model, empirical relationships are proposed that relate cyclone performance, filtration efficiency, and the diameter of the filtered particles. It is shown that optimal control of the air purification of the working area is possible only when cyclones with an adjustable inlet diameter are used as part of an automatic control system closed in terms of VOC concentration. 
Keywords: three-dimensional printing; volatile organic compounds; cyclone separator; automatic regulation; optimal control.