Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Digital Technologies Adoption in the Agro-Industrial Complex as a Priority of Regional Development in the Conditions of Global Macroeconomic Changes Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

Production Process Data as a Tool for Digital Transformation of Metallurgical Companies

verfasst von : Antonina Stoianova, Natalia Vasilyeva

Erschienen in: XIV International Scientific Conference “INTERAGROMASH 2021"

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Big Data analysis is becoming an everyday task for companies all over the world, including Russian companies. Due to advances in technology and the reduction in the cost of storage systems, companies can now collect and store large volumes of heterogeneous data. The important step of extracting knowledge and value from such data is a challenge that will eventually be met by all companies seeking to maintain their competitiveness and place in the market. However, companies face several challenges when it comes to collecting, pre-processing, and integrating data into cohesive data sets designed to deliver analytics. In this article, the above problems and possible solutions are illustrated using the example of cleaning, integration, and normalization of data obtained in the measurement of indicators for the Vanyukov melting furnace process. The article considers an approach to the study of metallurgical processes using the analysis of large operational control data sets. Standard methods of processing the data sets of operational process control are used. The correlation analysis of the main process parameters is carried out. The results are interpreted for their further practical application.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Forecasting Digital Economy Development Trends Based on Scientometric Data Monitoring
Nächstes Kapitel Reduction of Measurement Error of Axisymmetric Parts with an Optical System
Literatur
1.
Aazam, M., Zeadally, S., Harras, K.A.: Deploying fog computing in industrial internet of things and industry 4.0. IEEE Trans. Ind. Inform. 14, 4674–4682 (2018)
2.
Akberdina, V.: Transformation of the Russian industrial complex in the context of the digitalization of the economy. J. New Econ. 19(3), 82–99 (2018). (In Russian)
3.
Bartel, A., Ichniowski, C., Shaw, K.: How does information technology affect productivity? Plant-level comparisons of product innovation, process improvement and worker skills. Quart. J. Econ. 122(4), 1721–1758 (2007)CrossRef
4.
Ghobakhloo, M.: The future of manufacturing industry: a strategic roadmap toward Industry 4.0. J. Manuf. Technol. Manag. 29, 910–936 (2018)
5.
Capo, D., Levina, A., Dubgorn, A., Schröder, K.: Enterprise architecture concept for digital manufacturing. IOP Conf. Ser.: Mater. Sci. Eng. 1001(1), 012044 (2020)
6.
Castelo-Branco, I., Cruz-Jesus, F., Oliveira, T.: Assessing Industry 4.0 readiness in manufacturing: evidence for the European Union. Comput. Ind. 107, 22–32 (2019)
7.
Ghobakhloo, M., Fathi, M.: Corporate survival in Industry 4.0 era: the enabling role oflean-digitized manufacturing. J. Manuf. Technol. Manag. 30(8), 1–30 (2019)
8.
Kokolek, N., Jakovic, B., Curlin, T.: Digital knowledge and skills—key factors for digital transformation. In: Proceedings of the 30th DAAAM International Symposium, pp. 46–53 Vienna, DAAAM International (2019)
9.
Gogolinskiy, K.V., Syasko, V.A.: Prospects and challenges of the fourth industrial revolution for instrument engineering and metrology in the field of non-destructive testing and condition monitoring. Insight: Non-Destruct. Test. Cond. Monit. 61(8), 434–439+447 (2019)
10.
Heavin, C., Power, D.J.: Challenges for digital transformation—towards a conceptual decision support guide for managers. J. Decis. Syst. 27(Suppl. 1), 38–45 (2018)
11.
Idrisov, G.I., Knjaginin, V.N., Kudrin, A.L., Rozhkova, E.S.: New technological revolution: challenges and opportunities for Russia. Voprosy jekonomiki 4, 5–25 (2018)CrossRef
12.
Razmanova, S.V., Andrukhova, O.V.: Oilfield service companies as part of economy digitalization: assessment of the prospects for innovative development. J. Min. Inst. 244, 482–492 (2020)CrossRef
13.
Safiullin, R.N., Afanasyev, A.S., Reznichenko, V.V.: The concept of development of monitoring systems and management of intelligent technical complexes. J. Min. Inst. 237, 322–330 (2019)CrossRef
14.
Savchenkov, S.A., Bazhin, V.Y, Volkova, O.: Tendencies of innovation development of the Russian iron and steel industry on the base of patent analytics for the largest national metallurgical companies. CIS Iron Steel Rev. 20, 76–82 (2020)
15.
Smirnova, N.V., Rudenko, G.V.: Tendencies, problems and prospects of innovative technologies implementation by Russian oil companies. J. Ind. Pollut. Control 33(1), 937–943 (2017)
16.
Sony, M.: Industry 4.0 and lean management: a proposed integration model and research propositions. Prod. Manuf. Res. 6, 416–432 (2018)
17.
Zozulja, D. M.: Digitalization of the Russian economy and Industry 4.0: challenges and prospects. Voprosy innovacionnoj jekonomiki 8(1), 1–14 (2018)
18.
Semenova, I.N., Kirpichenkov, I.A.: Development of the control system for temperature conditions of melting process in the Vanyukov furnace Russian. J. Non-Ferrous Metals 5, 59–62 (2009)
19.
Vasilyeva, N.V., Fedorova, E.R.: Statistical methods of evaluating quality of technological process control of trends of main parameters dependence. J. Phys.: Conf. Ser. 1118(1), 012046 (2018)
20.
Acheson, C., Mackle, D., Murphy, A., Butterfield, J., Higgins, P., Collins, R., Tame, R.: Using design of experiments to define factory simulations for manufacturing investment decisions. In: Proceedings of the 34th International Manufacturing Conference, Sligo, Ireland (2017)
21.
Kabanov, E. I., Korshunov, G. I., Gridina, E. B.: Algorithmic provisions for data processing under spatial analysis of risk of accidents at hazardous production facilities. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu 2019(6), 117–121 (2019)
22.
Khudyakova, T.M., Kolesnikov, A.S., Zhakipbaev, B.E., Kenzhibaeva, G.S., Kutzhanova, A.N., Iztleuov, G.M., Zhanikulov, N.N., Kolesnikova, O.G., Mynbaeva, E.: Optimization of raw material mixes in studying mixed cements and their physicomechnical properties. Refract Ind Ceram 60, 76–81 (2019)CrossRef
23.
Kolesnikov, A.S.: Kinetic investigations into the distillation of nonferrous metals during complex processing of waste of metallurgical industry. Russian J. Non-Ferrous Metals 56(1), 1–5 (2015)
24.
Zhukovskiy, Y., Korolev, N., Koteleva, N.: About increasing informativity of diagnostic system of asynchronous electric motor by extracting additional information from values of consumed current parameter. J. Phys.: Conf. Ser. 1015(3), 032158 (2018)
25.
Beloglazov, I.I., Petrov, P.A., Gorlenkov, D.V.: Development of an algorithm for control metallurgical processes of fluidized roasting using an adaptive controller. J. Phys.: Conf. Ser. 1059(1), 012015 (2018)
26.
Pivovarova, I.I.: Cluster analysis for structuring spatial data. Int. J. Mech. Eng. Technol. 10(2), 1102–1109 (2019)
27.
Marchenko, R.S., Cherepovitsyn, A.E.: Improvement of the quality of calculations using the Monte Carlo simulation method in the evaluation of mining investment projects, pp. 247–251 (2017)
28.
Oprea, G., Andrei, H.: Power quality analysis of industrial company based on data acquisition system, numerical algorithms and compensation results. In: 2016 International Symposium on Fundamentals of Electrical Engineering, ISFEE 2016, vol. 7803232 (2016)
29.
Kitrar, L.A., Lola, I.: Features of market measurement of digital activity of entrepreneurs in Russia: approach, indicators, pilot results. Voprosy statistiki 26(8), 28–42 (2019)CrossRef
30.
Levina, A.I., Borremans, A.D., Lepekhin, A.A., Kalyazina, S.E., Schroder, K.M.: The evolution of enterprise architecture in scopes of digital transformation. IOP Conf. Ser.: Mater. Sci. Eng. 940(1), 012019 (2020)
31.
Liere-Netheler, K., Packmohr, S., Vogelsang, K.: Drivers of digital transformation in manufacturing. In: Proceedings of the 51st Hawaii International Conference on System Sciences, pp. 3926–3935 (2018)
32.
Beloglazov, I.I., Boikov, A.V., Petrov, P.A.: Discrete element simulation of powder sintering for spherical particles. In: Key Engineering Materials (KEM), vol. 854, pp. 164–171 (2020)
33.
Litvinenko, V.S.: Digital economy as a factor in the technological development of the mineral sector. Nat. Resour. Res. 29(3), 1521–1541 (2020)
34.
Machado, G., Winroth, M., Carlsson, D., Almström, P., Centerholt, V., Hallin M.: Industry 4.0 readiness in manufacturing companies: challenges and enablers towards increased digitalization. CIRP Manuf. Syst. Conf. 81, 1113–1118 (2019)
Metadaten
Titel
Production Process Data as a Tool for Digital Transformation of Metallurgical Companies
verfasst von
Antonina Stoianova
Natalia Vasilyeva
Copyright-Jahr
2022
Buch
XIV International Scientific Conference “INTERAGROMASH 2021"

Print ISBN: 978-3-030-81618-6

Electronic ISBN: 978-3-030-81619-3

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-030-81619-3_87

Premium Partner

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen. 

    Zur Marktübersicht
    Bildnachweise