No 1 (2021)

OPTIMIZING THE CONTENT OF THE SEDIMENTS IN THE PROCESS OF HUDRON'S HYDROCRACKING WITH THE USE OF MACHINE LEARNING METHODS
Nuzhny A.S., Odnolko I.S., Glukhov A.Y., Butyrin M.S., Levchenko E.N., Starikov A.S., Karasev I.V., Lapinova S.A.

Abstract

The paper proposes a mathematical model to optimize the operation of the tar hydrocracking unit. The purpose of modeling is to improve the economic effect of product output by selecting optimal parameters, such as hydrogen flow rate and reactor temperature. Hot Filtered Precipitation (HFT) is used as a target. The model involves the search for the minimum value of the functional with restrictions presented in the form of a fine imposed when the parameters go beyond the permissible values, as well as when the target parameter deviates from the specified value. The execution of the algorithm includes two stages. The first stage is the simulation of the HFT value for a given state of the installation at the selected parameters of temperature and hydrogen flow rate using a virtual analyzer, the second stage is to solve the optimization problem by selecting the control parameters of the installation. For the first stage, a model for assessing the HFT indicator by technological indicators was built, including the main factors determining it; machine learning methods were used to find the parameters of the models. The free standard library of optimum search tools scipy.optimize was used to solve the optimization problem. Powell's algorithm was chosen as the optimization method. The paper presents the results of testing the model on real data provided by an oil refinery in the city of Burgas in Bulgaria. The study period includes several operating modes of the installation, in particular, the intensive load mode during 2018-2019 and low load during the 2020 period. The results of testing the model on real data presented in the work have been verified by experts in the field of oil refining for compliance with real conditions.
Прикладная математика и вопросы управления / Applied Mathematics and Control Sciences. 2021;(1):7-22
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STUDY ON NEURAL NETWORK MODEL TO DETECT ANOMALIES IN DATASETS
Sheglevatych R.V., Sysoev A.S.

Abstract

Digitalization of various spheres of economic and social life is accompanied by the emergence of large amounts of data, processing of which is necessary to identify certain dependencies, build models of processes and systems. The study is devoted to the development and research of a mathematical model for the classification of data on medical care in the medical organization of Lipetsk region. As inputs there were used indicators of medical care, divided into five groups (data describing patient; data describing the medical organization in which the care was provided; indicators of the disease; data on health employee that assisted; indicators characterizing the specific features of the patient's visits to a particular specialist). The volume of records on which the study was conducted is more than one million records of the facts. The purpose of the study is to propose models and approaches for identifying erroneous records, as well as cases of falsification. The paper presents a statement of the binary classification problem. Anomaly detection refers to the problem of finding data that does not correspond to some expected process behavior or indicator that occurs in the system. When building systems for detecting anomalous observations, much attention must be paid to the model underlying the system. The study is devoted to the construction of a model for detecting anomalous values of a fixed indicator based on a combination of an isolation forest algorithm to estimatie the observation anomaly index and the subsequent application of a neural network classifier. The study contains the results of computational experiments to determine the threshold value for dividing records into classes of anomalous observations and data that do not have signs of abnormality. To evaluate which factors should be passed to the input of the neural network classifier (in order to increase the time efficiency of data processing), the approach to the reduction of the neural network model based on Sensitivity Analysis was proposed. The classical approach when considering the sensitivity of systems is to find the sensitivity by the parameter of the system under study, however, there is also a direction of Sensitivity Analysis that involves using its factors as the estimated parameters of the system. The proposed approach is based on applying Analysis of Finite Fluctuation. This analysis is based on replacing the mathematical model of the dependence of the system output on factors with a model of the dependence of the finite fluctuation in output on the finite fluctuations of factors. In Mathematical Analysis such a structure is known - this is Lagrange mean value theorem. The approach allows us to determine the values of the so-called factor loads. The paper presents a new approach to averaging the obtained values of factor loads and constructing interval characteristics for their estimation. A study of the stability of the proposed procedure for calculating the sensitivity coefficients of the model is presented.
Прикладная математика и вопросы управления / Applied Mathematics and Control Sciences. 2021;(1):23-40
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MODELS AND ALGORITHMS TO CONTROL THE SELECTION OF RELIEF VALVES FOR CHEMICAL PRODUCTION EQUIPMENT
Nikolin A.V., Moshev E.R.

Abstract

The article is devoted to the topic of automation of control of the selection process or determination of the technological and structural characteristics of safety valves installed on the technological equipment of chemical plants to ensure their industrial safety. The purpose of the work was to develop models and algorithms that allow automating the determination of the above characteristics. The tasks necessary to achieve this goal have been formulated. The methods of systems analysis and the theory of artificial intelligence, as well as the methodology of structural analysis and design, functional modeling, modular and object-oriented programming, are used. The analysis of scientific and technical literature on the research topic is carried out. As a result of the analysis, no models and algorithms were identified that would automate the determination of the above characteristics. Using a systematic approach, the analysis of the process of defining the characteristics of safety valves as an object of computerization is carried out. As a result of the analysis, it was found that this process contains heuristic knowledge and can be formalized using the methods of the theory of artificial intelligence. Using the methodology of structural analysis and design, functional modeling, as well as the basic principles of system analysis, a logical-informational model for determining the technological and structural characteristics of safety valves as an organizational and technological process has been developed. Using the methods of the theory of artificial intelligence, production models have been developed for representing knowledge about safety valves; gases used in the chemical industry; design factors required to determine the desired characteristics of the safety valves. Heuristic-computational algorithms have been developed for determining the technological and design characteristics of safety valves, including the nominal pressure of the valve, the highest overpressure downstream of the valve, possible values of the nominal pressure of the outlet pipe, and the effective area of the valve "seat". The developed models and algorithms are supposed to be applied to create a problem-oriented system that will ensure the determination of the technological and structural characteristics of safety valves in an automated mode, which will significantly reduce the time spent on the procedure for selecting a brand of safety valve that meets industrial safety requirements and will also improve the quality of this procedure. ... The practical application of the created problem-oriented system will increase the economic efficiency and industrial safety of the operation of chemical plants in general. The developed models and algorithms can also be used as examples in solving the problems of automation of determining the characteristics of safety valves in other industries.
Прикладная математика и вопросы управления / Applied Mathematics and Control Sciences. 2021;(1):43-56
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NONGENTROPIC APPROACH TO ESTIMATING THE LEVEL OF INTELLIGENCE OF CONTROL SYSTEMS IN DECISION-MAKING TASKS
Kharitonov V.A., Krivogina D.N., Spirina V.S.

Abstract

A new multi-model approach to assessing the influence of the level of intellectualization of target selection procedures on a set of alternatives implemented through the use of information management systems is proposed. The methodological basis of decision-making procedures in these systems is human intuition, which divided into two types: Inside and Intueri. The first type related to the innate natural ability of people to make choices, based on their acquired life experience and is the most common. The second type constructed in the information environment as a composition/superposition of Inside options in order to increase the level of intellectualization of target choosing procedures. It is suggested that in developing rules of output stages of decision making they should be evaluated negentropically, the unit of measurement that should be "bit". After generalizing all the output procedures, this will allow a comparative analysis of the levels of intellectualization of ranking procedures, which are a preliminary stage in the implementation of the choosing process. Multivariance of solutions to the set tasks requires solving the problem of listing effective choices, their formalizing, modeling, and developing effectiveness criteria. To solve these problems, proposed a new multi-model approach. These approaches include linguistic contextual representations of ranking procedures based on the set of meanings (semantics) and "triangles" by N. Chomsky; formal systems for listing effective ranking procedures based on generating context-free grammar in the form of metalinguistic formulas and variables; mathematical algorithms for evaluating the levels of intellectualization of choice procedures using the negentropic principle. Solution of both simple and complex tasks selection is represented as a formal output system. The focus is on three classes of intelligent selection procedures: intuition-based selection of the Inside type; procedures for building artificial intelligence with the connection of intuition like Intueri; intelligent procedures for choosing alternatives using artificial intelligence. The classes of these intellectual procedures are presented as the resultant conclusions of the same "theorem", but differ in the efficiency of performing ranking / selection tasks and the level of their intellectualization. The solution of a simple choice problem characterized by low speed, low level of non-manipulability and documentability. Low effectiveness of ranking procedures leads to the need to introduce a large number of restrictions on the complexity parameters of the choice problems to solve. A high level of intellectualization of this procedure can be reveal in a variety of classes of problems to solved, which is due to the erudition of the subject of control. Realization of the second class of intelligent procedures can become the basis for the development of various options for using artificial intelligence. When once obtained algorithmic constructions can be available for repeated use by organizing standard access to these models. There will be an effect of "amortization" as an expenditure of intellectual resource when delegating authority for a new or repeated assignment. Intelligent alternative selection procedures using artificial intelligence are decision-making algorithms designed for multiple use, within its "authority" as a limited subject area.
Прикладная математика и вопросы управления / Applied Mathematics and Control Sciences. 2021;(1):59-80
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INTELLIGENT DECISION-MAKING SUPPORT ALGORITHMS FOR HEALTH-CARE INSTITUTIONS
Larionova R.A.

Abstract

The aim of the article is to develop algorithms of intellectual support for managerial decision-making in a preventive medical institution by medical staff on loading/reserving the number of patients to be served. The article substantiates the relevance of improving the mechanisms of management of non-stationary processes of medical services by health care institution (HCP) on the basis of subject-oriented modeling of its activities as a system of mass service. The article offers tools for an integrated assessment of the current state of LPI as a socio-economic system for the substantiation of necessary measures to ensure the required level of readiness of LPI. The article touches upon the problem of determining the functional completeness of LFU, and in this connection, the range of seasonal planning is considered. The paper highlights the issues of the coordination procedure in the formation of a comprehensive assessment of LPF on the reserve/loading issues. The author proposes a mechanism for processing the data coming from LPFs according to the predicate, on the basis of which an automated data processing procedure can be built as an addition in the formation of a comprehensive assessment of LPF load/reserve. These algorithms are scientifically new and make it possible to monitor the current state of the mass service system and predict the functional completeness/incompleteness of the system with justification of necessary correction of its parameters. In the article the analysis of occurrence of typical problems of identification of typical situations and recommended actions for bringing LRC to a new state, in the best way, corresponding to the task of the guaranteed granting of medical services to the population before the moment of the new primary information is resulted. The author has proposed the use of simulation results in the formation of a functional management of the state of health care facilities. The proposed intelligent control mechanisms ergonomically well match the capabilities of the personnel of usual qualification.
Прикладная математика и вопросы управления / Applied Mathematics and Control Sciences. 2021;(1):81-94
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ESTIMATION OF A POSSIBILITY OF PRESERVING ORDER IN A STATE WITH USAGE OF ARTIFICIAL NEURAL NETWORK
Permyakov A.M., Yasnitsky L.N.

Abstract

A political science-mathematical model has been built, designed to assess the possibility of maintaining order in the state. The model is based on a neural network trained on data on the state of many countries in different historical periods. The adequacy of the model is shown by comparing the simulation results with the real course of historical processes. The model was used to estimate the significance of the input parameters. It was found that the most significant parameters that have the greatest impact on the situation in the country are taxes, the state of the economy, and the availability of essential goods. The influence of the most significant parameters on the course of historical events is demonstrated by examples of the situation in France in the period 1629-1634 and in the Ottoman Empire during 1799-1804. Computer experiments were carried out by the scenario forecasting method: using a neural network, calculations were performed by enumerating the values of one parameter, or a small fixed group of parameters, while the values of the other parameters remained unchanged. An attempt has been made to predict the development of the situation in Venezuela for the next five years. A large number of options for forecasts of the development of events were carried out, depending on various combinations of possible measures taken by the government to stabilize the situation. Based on these forecasts, the most effective measures were selected to reduce tensions in the country. The practical value of the study lies in the fact that the created political science and mathematical model can be used to assess the possibility of maintaining order in modern states and to determine the steps necessary to prevent or trigger revolutions, civil wars, etc.
Прикладная математика и вопросы управления / Applied Mathematics and Control Sciences. 2021;(1):95-118
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DEVELOPMENT OF ARCHITECTURE OF MESSAGE QUEUE BASED INFORMATION SEARCH SYSTEMS FOR ENTERPRISE INFORMATION SYSTEMS
Loginovskiy O.V., Shinkarev A.A., Koval M.E.

Abstract

Today, information search has become not only an ordinary skill for users of information systems, but also an essential part of business. The amount of data that organizations own is growing day by day. Accordingly, it becomes difficult to find the necessary information in this data, especially if it is allocated in different storages. To facilitate the search in enterprise information systems, information search systems with a single graphical interface and ability to search in different data sources are used. There are ready-made software packages on the market, though some companies opt out to develop their own search engine. In the latter case, however, it is critical to be scrupulous designing the system architecture to comply with all the requirements that are imposed on this kind of software today. Information search systems for enterprise information systems. The purpose of the study was to describe the main architectural approaches to developing information search systems and identify their strong and weak points; to form basic ideas for the development of the architecture of information search systems based on message queues and to describe the main components of such systems; to elaborate on the key aspects of the practical implementation of the data warehouse of user search queries and the results of their processing. The paper considers the results of studies on the typical problems which employees face during information search in enterprise applications. The authors evaluate the existing architectural approaches to information search systems, analyze and compare two of the most popular message brokers. The article substantiates the relevance of the problem of finding information in enterprise information systems. The authors provide a description of the main approaches to the architecture of information search systems, go into their advantages and disadvantages, and provide architectural diagrams of the components. The microservices that make up a message queue-based system are described. Kafka is chosen and substantiated as the most suitable message broker. The authors also give a graphical scheme of handling errors that arise during search services operation.
Прикладная математика и вопросы управления / Applied Mathematics and Control Sciences. 2021;(1):119-140
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STOCHASTIC FRONTIER METHOD FOR EVALUATING THE EFFICIENCY OF ENTERPRISES
Alimkhanova A.N., Mitsel A.A.

Abstract

At the moment, there is an increase in the importance of efficiency in another sector of the economy. Evaluating the efficiency of an enterprise makes it possible to implement a correct and profitable resource allocation strategy, which shows its potential level. In connection with the annual increase in the number of bankrupt enterprises, the problem of assessing the efficiency of enterprises' activities is relevant both for their owners and managers, and for creditors. There are various methods and models for assessing the efficiency of enterprises. One study focuses on the improvement of a parametric method for assessing the performance of objects - the SFA (Stochastic Frontier Analysis) method. The classical SFA method is based on the production function of an enterprise, linking the volume of output with the volume of consumed resources. In this case, the SFA model uses several input (volumes of consumed resources) and only one output parameter - the volume of output. The essence of the proposed modification of the model based on the SFA method is to use financial indicators of enterprises' activities instead of data on consumed resources and output, on the basis of evaluating the financial activity of the economic objects under study. The developed model of the situation for assessing the efficiency of the enterprise in terms of its financial indicators.
Прикладная математика и вопросы управления / Applied Mathematics and Control Sciences. 2021;(1):143-155
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BOOTSTRAP REGRESSION BASED ON THE MAXIMUM ENTROPY IN FORECASTING SOCIO-ECONOMIC INDICATORS
Kislyakov A.N.

Abstract

The work is devoted to the actual problem of creating predictive models and evaluating their adequacy based on bootstrap-the method of virtual expansion of statistical sampling. One of the modified bootstrap algorithms based on the entropy maximum is used as an approach for constructing probabilistic statements. The bootstrap method allows you to simulate a large number of random samples within the forecast interval based on the initial sample, represented as a time series of changes in a particular indicator, and evaluate the statistical characteristics of the indicator of interest. The main problem of time series forecasting using the bootstrap method is the need to preserve the dependence of the current indicator value on previous observations, as well as to take into account other factors that affect the process, i.e. the use of regression models. The maximum entropy bootstrap method allows you to generate samples for each indicator that satisfy the ergodicity theorem, while preserving the original form and time dependence of the autocorrelation and partial autocorrelation functions. The maximum entropy bootstrap algorithm is used for cases when the time series is short, non-stationary, with sharp changes in the values of variables and discontinuities. The paper provides an example of using the bootstrap method based on the maximum entropy in relation to the task of creating predictive models to assess their adequacy, which allow us to forecast the indicators of imports and exports of the Russian Federation in billions of us dollars. under the conditions of observing the ruble exchange rate against the us dollar, as well as the indicator of the cost of a barrel of oil on the world market. The results obtained allow us to conclude about the advantages of the considered approach to implementing bootstrap regression for creating predictive models.
Прикладная математика и вопросы управления / Applied Mathematics and Control Sciences. 2021;(1):156-173
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FINITE DIFFERENCES METHOD FOR SOCIO-ECONOMIC MODELING
Sirotina N.A., Kopoteva A.V., Zatonskiy A.V.

Abstract

In the issue we consider socio-economic processes modeling based on first and second order finite differences models. Since commonly used modeling methods have drawbacks and thus are not universal it was necessary to develop alternative methods which are better in some aspects. Specifically multiple linear regression models have limited prediction abilities, and differential regression coefficient evaluation method is quite complex and have some economically uninterpreted excess tunings. In our research we replaced first and second order derivatives in differential regression models with their finite differences equivalents and thus gained a multiple linear regression model modification which includes first and second order auto regression items. Estimation of their parameters can be done using a modification of least-squares method in which we demand that factor coefficients signs for models with and without auto regression items are the same. Due to additional items in the modified linear regression models their approximation capacity is greater than of a common model. However for application purposes model forecasting capacity is more important, i.e. the forecasting efficiency criterion is the most significant for a decision making. In order to estimate forecasting potential of modified multiple linear regression models we performed coefficient estimation of unmodified and modified equations for 59 various socio-economic data sets. We used shortened time series, so we could calculate model values and compare them to actual data. It was determined that modified multiple linear regression models allowed to make better predictions in 49 (76.3 %) cases. We can now assume that addition of auto regression items into multiple linear regression model can increase short-term forecasting efficiency.
Прикладная математика и вопросы управления / Applied Mathematics and Control Sciences. 2021;(1):174-189
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