Improving the energy efficiency of chemical industries and increasing their environmental friendliness requires an assessment of the parameters of consumption and losses of energy resources. The aim of the study is to develop and test a method for solving the problem of optimizing the use of energy resources in chemical production based on the methodology of descriptive statistics and training of neural networks. Research methods: graphic and tabular tools for descriptive data analysis to study the dynamics of the structure of energy carriers and determine possible reserves for reducing their consumption; correlation analysis with the construction of scatter diagrams to identify the dependences of the range of limit values of electricity consumption on the average rate of energy consumption; a method for training neural networks to predict the optimal values of energy consumption; methods of mathematical optimization and standardization. The authors analyzed the trends in the energy intensity of chemical industries with an assessment of the degree of transformation of the structure of the energy portfolio and possible reserves for reducing the specific weight of electrical and thermal energy; determined the dynamics of energy losses at Russian industrial enterprises; established the correlation dependence of the range of limiting values of power consumption on the average rate of power consumption; determined the optimal limiting limits of the norms for the loss of electrical energy by the example of rubbers of solution polymerization. The results of the study can be used in the development of software complexes for intelligent energy systems that allow tracking the dynamics of consumption and losses of energy resources. Using the results allows you to determine the optimal parameters of energy consumption and identify reserves for improving energy efficiency.

The planning and management of traffic flow networks with multiple input data sources for decision-making generate the need for a mathematical approach. The program of measures for the development of the transport infrastructure of the Russian Federation provides for the selection of pilot regions for the creation of intelligent transportation systems. With extensive knowledge of theoretical and applied mathematics, it is important to select and adapt mathematical methods for solving problems. In this regard, the aim of the study is to develop and validate an algorithm for solving the problem of classifying objects according to the potential of creating intelligent transportation systems. The main mathematical apparatus for classification is the «random forest» machine learning algorithm method. A bagging machine learning meta-algorithm for high accuracy of the algorithm was used. This paper proposes the author’s method of sequential classification analysis for identifying objects with the potential to create intelligent transportation systems. The choice of using this method is justified by its best behavior under the large number of predictor variables required for an objective aggregate assessment of digital development and quality of territories. The proposed algorithm on the example of Russian regions was tested. A technique and algorithm for statistical data processing based on descriptive analytics tools have been developed. The quality of the classification analysis algorithm was assessed by the random forest method based on misclassification coefficients. The admissibility of retrained algorithms and formation of a «fine-grained» «random forest» model for solving classification problems under the condition of no prediction was proven to be successful. The most productive models with the highest probability of correct classification were «reached» and «finalized» on the basis of logistic regression analysis of relationships between predictors and categorical dependent variables. The regions of class 1 with «high potential for the creation of intelligent transportation systems» are most likely to be ready for the reorganization of infrastructure facilities; the introduction of digital technologies in the management of traffic flows was found.

Целью статьи является исследование уровня и динамики энергоемкости полимерной продукции, оценка потенциала использования полимерных отходов в качестве источника тепловой энергии в экономике замкнутого цикла. В качестве основных методов исследования предложен авторский методический инструментарий, использован системный подход, методы дескриптивной статистики, графического анализа, причинно-следственных связей. Произведен анализ текущих тенденций с расчетом уровня энергоемкости полимерной продукции, позволивший отнести исследуемые производства к 1 классу энергоемкости «Высокая энергоемкость». В производстве полимерной продукции выделены три вида отходов (пластик, резина, термореактивные смолы), являющиеся потенциальным сырьем для получения вторичной тепловой энергии. Показано, что значения теплоты сгорания отходов (31-45 ГДж/т), способы получения энергии и их эффективность (45-70%) позволяют использовать данные виды отходов для разработки решений по альтернативным источникам энергии. Определен масштаб образования отходов полимеров на российских предприятиях, позволяющий сделать вывод о целесообразности разработки решений по альтернативным источникам энергии. Разработан алгоритм расчета объема вторичной тепловой энергии, направленный на решение двух задач: задачи определения объема, полученного тепла путем энергетического рециклинга отходов; задачи определения возможного объема продукции, произведенной на основе вторичной тепловой энергии. Апробация алгоритма на примере отходов пластика и использования тепловой энергии от их сжигания показала возможность выработки из отходов 56467,2 Гкал тепловой энергии и производства с ее использованием 26516,7 т синтетических смол и пластмасс. Рекомендовано при организации энергетического рециклинга соблюдать приоритет использования отходов непосредственно в производственном цикле в качестве вторичного сырья с учетом экологической и экономической целесообразности.

The article is devoted to the study of environmental problems in the production of steel pipes and the rational use of waste. The relevance of the study lies in the high resource intensity and waste of pipe metallurgy, the classification of steel production and their subsequent redistribution as the first category of environmental hazard and is assigned to the areas of application of the best available technologies. The aim of the article is to analyze the resource intensity and waste production of steel pipes, to study the existing waste management system and its rationality at Russian enterprises. As the main research methods, the IDEF0 modeling methodology was used to visualize the waste management system and to structurize the model for the production of welded pipes taking into account emissions of pollutants, as well as Data Mining technology in the form of a classification and regression tree for studying trends and local patterns in waste management. The tendency of increasing resource intensity at the enterprise of the pipe industry with relative financial instability is revealed. A visualization of the waste management system in the production of steel pipes is proposed, which makes it possible to distinguish the main functional blocks of the incoming and outgoing parts of the waste balance. A model of the processes of production of welded pipes is structured taking into account emissions of polluting substances into the atmosphere and cleaning methods according to the reference book on the best available technologies ITR 27-2017. Based on the constructed decision tree, knowledge was obtained on the trends in the treatment of steel pipe production waste. In order to reduce the level of rejection in the production of steel pipes and minimize the formation of scrap, a project has been proposed on the automation of technical quality control and the implementation of the MES system.

The safe development of chemical industries requires adequate control of the environmental sustainability of the areas where enterprises are located. The purpose of the article is to develop and test a methodology for solving the multicriteria problem of choosing industrial zones for the development of chemical industries using the method of an additive global criterion. The novelty of the methodology lies in the multi-criteria and complexity of the tool and the presence of a statistical base, which allows it to be used for various socio-economic purposes at all levels of government. As the main research tools, the methods of multi-criteria selection of objects, one-dimensional data scaling, additive convolution of criteria, and methods of multivariate statistical analysis for verifying the results obtained and making a decision were used. The article describes the mathematical apparatus of the technique for solving the multicriteria problem of selecting objects by the method of an additive global criterion. The solution algorithm provides for a three-level integration of particular indicators using the methods of mathematical processing of an array of different-dimensional values. The procedure for selecting the vectors of the criterion space makes it possible to select industrial zones and obtain a global criterion using the additive convolution method. In order to test the methodology, the problem of choosing industrial zones for the potential development of chemical industries in the Russian region was solved. For the development of chemical production, industrial zones have been selected that are included in the above-average environmental sustainability group: Bavlinskaya, Nurlatskaya, Bugulminskaya, and Leninogorskaya. Tendencies of decrease in ecological stability of the zones, which have relatively safe industries on their territory but are adjacent to the zones of location of environmentally unfavorable industries, are revealed. The materials of the article can be used in the development of intelligent systems for monitoring and controlling the development of chemical industries, which allow monitoring the level of environmental safety of industrial zones, identifying sources of negative environmental impact with pursuing decision-making on the organization and planning of production systems in the territorial space.