Application of modeling tools in the framework of smart petrochemical production
( Pp. 46-58)

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Nurgaliev Rustam K. kandidat tehnicheskih nauk, docent; zaveduyuschiy kafedroy sistem avtomatizacii i upravleniya tehnologicheskimi processami
Kazan National Research Technological University Shinkevich Alexey I. doktor tehnicheskih nauk, professor; zaveduyuschiy kafedroy logistiki i upravleniya
Kazan National Research Technological University
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Abstract:
The purpose of the research. The research is aimed at identifying the possibilities of using various modeling tools in the framework of the functioning of “smart” petrochemical production. To achieve the goal contributed to a number of tasks: to study the possibility of applying IDEF0 methodology in terms of building a “smart” production, to build the decomposition process, the introduction of new equipment in a “smart” the petrochemical industry in BPMN notation, to explore the specific neural network modeling and to develop a neural network model to predict energy consumption of petrochemical plants. Results. It is summarized that modeling is an integral element of the design and management of “smart” petrochemical production, provides process optimization, rationalization of the information and communication environment of the enterprise, energy saving, improving the quality of petrochemical products, production efficiency, reducing the negative impact on the environment; “smart” production should be accompanied by the development of a mechanism for digitalization of production processes, regulation of the algorithm for building cyberspace. We need an integrated approach to production modeling, which involves end-to-end data management at all stages of production, using various modeling methods, and consolidating the results of modeling in a single database, which serves as a relevant empirical basis for making rational management decisions. The study developed a scheme to enter the production assets to the smart production (BPMN notation), a flow of the process, taking into account the difficult situation related to the operation of production equipment; a logical and informational model of the formation of a “smart” petrochemical production (in the IDEF0 notation) is constructed, which takes into account the relationships between sub-processes and the potential effect, allowing for the development of instructions and methodological materials for the modernization of petrochemical production; A predictive model for regulating the level of energy consumption by petrochemical enterprises, depending on the costs of technological innovations and on the volume of polluting emissions generated by industrial enterprises, is proposed, based on training neural networks of different architectures and allowing, in accordance with the activation function of the hidden layer of the neural network, to identify trends in energy consumption.
How to Cite:
Nurgaliev R.K., Shinkevich A.I., (2021), APPLICATION OF MODELING TOOLS IN THE FRAMEWORK OF SMART PETROCHEMICAL PRODUCTION. Computational Nanotechnology, 1 => 46-58. DOI: 10.33693/2313-223X-2021-8-1-46-58
Reference list:
Abramova I.G., Abramov D.A. Povyshenie effektivnosti proizvodstvennykh moshchnostey v svete realizatsii tekhnologiy berezhlivogo i umnogo proizvodstva // Izvestiya Samarskogo nauchnogo tsentra Rossiyskoy akademii nauk. 2013. T. 15. № 6-3. S. 557-562.
Akhmadiev F.G., Malanichev I.V. Neyrosetevye algoritmy topologicheskoy optimizatsii v zadachakh gidrodinamiki // Vestnik Tekhnologicheskogo universiteta. 2019. T. 22. № 7. S. 110-113.
Barvinok V.A. Smelov V.G., Kokareva V.V., Malykhin A.N. Postroenie umnogo proizvodstva na baze additivnykh tekhnologiy // Problemy mashinostroeniya i avtomatizatsii. 2014. № 4. S. 142-149.
Birbraer R. Moskovchenko A., Busov S., Novikov D. K umnomu proizvodstvu cherez ob edinenie vozmozhnostey // SAPR i grafika. 2009. № 4 (150). S. 54-57.
Brendl D. Umnoe proizvodstvo: konvergentsiya razlichnykh sostavlyayushchikh // Control Engineering Rossiya. 2016. № 6 (66). S. 26-29.
Vershinin A.N., Karamysheva E.O. Osobennosti razrabotki matematicheskoy modeli protsessa obucheniya neyronnoy seti dlya kontrolya zashchishchennosti avtomatizirovannykh informatsionnykh sistem // Computational Nanotechnology. 2019. № 1. S. 39-43.
Kozak N.V., Nezhmetdinov R.A., Martinova L.I. Integratsiya dannykh sistem logicheskogo upravleniya v umnoe proizvodstvo na osnove kontseptsii Industry 4.0 // Avtomatizatsiya v promyshlennosti. 2018. № 5. S. 11-15.
Mokshin V.V., Kirpichnikov A.P., Maryashina D.N. i dr. Sravnenie sistem strukturnogo i imitatsionnogo modelirovaniya Stratum 2000, SIMULINK, AnyLogic // Vestnik Tekhnologicheskogo universiteta. 2019. T. 22. № 4. S. 144-148.
Nigmatullin V.R., Rudnev N.A. Ispol zovanie metodov mashinnogo obucheniya i iskusstvennogo intellekta v khimicheskoy tekhnologii. CH. II // Elektronnyy nauchnyy zhurnal Neftegazovoe delo . 2019. № 5. S. 202-238.
Novikova S.V., Tunakova YU.A., SHagidullin A.R., Kuznetsova O.N. Proektirovanie i obuchenie neyroseti dlya rascheta kontsentratsiy metallov, postupayushchikh ot peredvizhnykh istochnikov zagryazneniya, na primere g. Kazani // Vestnik Tekhnologicheskogo universiteta. 2016. T. 19. № 24. S. 123-125.
Nurgaliev R.K., Gaynullina A.A., Ryzhov D.A. Uchebnyy programmnyy kompleks Avtomatizirovannaya sistema upravleniya predpriyatiem // Vestnik Tekhnologicheskogo universiteta. 2017. T. 20. № 18. S. 130-134.
Panchenko A.A., Rakhman P.A., Safarov A.M. Neyrosetevye modeli prognozirovaniya urovnya zagryazneniya atmosfernogo vozdukha v promyshlennom gorode // Sovremennaya nauka: aktual nye problemy teorii i praktiki. Seriya: Estestvennye i tekhnicheskie nauki. 2018. № 5. S. 121-126.
Petrishchev I.O., SHubovich V.G., Fedorova E.A., Znaenko N.S. Modelirovanie protsessov ob ektov skladov nefteproduktov na osnove metodologii IDEF0 // Sb. mater. Vseros. zaochnoy nauch.-prakt. konf. Obrazovanie i informatsionnaya kul tura: teoriya i praktika . Ul yanovsk: Ul yanovskiy gos. ped. un-t im. I.N. Ul yanova. 2016. S. 113-117.
Suchkov M.A., Galimulina F.F. Printsipy upravleniya kriptodannymi v ramkakh innovatsionnogo razvitiya informatsionnoy sredy predpriyatiya // Nauka i biznes: puti razvitiya. 2020. № 5 (107). S. 152-154.
Farakhov M.I., Laptev A.G., Basharov M.M. Modernizatsiya apparatov ochistki zhidkostey ot dispersnoy fazy v neftekhimicheskom komplekse // Teoreticheskie osnovy khimicheskoy tekhnologii. 2015. T. 49. № 6. S. 635-643.
Farakhov M.I., Laptev A.G., Basharov M.M. Import Substitution of Industrial Devices for Gas Purification from the Disperse Phase in Petrochemical Industry // Chemical and Petroleum Engineering. 2016. Vol. 52. Is. 5-6. Pr. 316-319.
Shinkevich A.I., Shaimieva E.Sh., Malysheva T.V., Gumerova G.I. Information system of decision support in the management environment of ecological project // Academy of Strategic Management Journal. 2020. T. 19. № 5. S. 1-11.
Keywords:
BPMN, IDEF0, “smart” manufacturing, petrochemical enterprise, modeling, BPMN, IDEF0, neural networks.


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