Steel in Translation

, Volume 47, Issue 8, pp 538–543 | Cite as

Software for decision-making support in blast-furnace operation

  • V. V. Lavrov
  • N. A. Spirin
  • I. A. Gurin
  • V. Yu. Rybolovlev
  • A. V. Krasnobaev
Article
  • 17 Downloads

Abstract

Experience shows that the successful introduction of automated information systems at metallurgical enterprises largely depends on the technology and software selected. In the present work, the basic technology and software options available are briefly outlined. The starting point is Agile development, which is based on iterative procedures, the dynamic formation of user requirements, and their implementation through constant dialog within working groups consisting of various specialists (users, analysts, programmers, and testers). Iteration corresponds to relatively brief development times (as a rule, months), after which the user is given the next tested version of the software, with new functional properties. The list of additional functional properties in each new version represents user priorities and is drawn from the overall list of requirements before each iteration begins. In each iteration, the following procedures are completed in sequence: verification of the computational algorithm (with the introduction of new variables, where necessary); functional modeling of the system; improvement of subsystem structure; conceptual modeling of the database; generation of a model of the database; loading of the test data in the database; creation of the functional diagrams in the mathematical library; implementation of the subsystem’s client software; testing and debugging of the software; and the development of reference documentation. The Atlassian JIRA system is used to control individual tasks and monitor their overall realization within the process of collective software development. The Atlassian Bitbucket platform provides remote storage for code storage and control of the software version. On the basis of up-to-date approaches to software development, systems that are functional, reliable, east to use, expandable, and integrable may be created. Such systems are characterized by minimum risk and acceptable cost.

Keywords

automated information system information and modeling systems software development stages flexible development methodology CASE tools version control blast-furnace production 

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References

  1. 1.
    Vyatkin, V., Software engineering in industrial automation: state-of the-art review, IEEE Trans. Ind. Inf., 2013, vol. 9, no. 3, pp. 1234–1249.CrossRefGoogle Scholar
  2. 2.
    Dimitrov, B.H., Nenov, H.B., and Marinov, A.S., Comparative analysis between methodologies and their software realizations applied to modeling and simulation of industrial thermal processes, 36th Int. Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2013), Opatija, Croatia, May 20–24, 2013, Piscataway, NJ: Inst. Electr. Electron. Eng., 2013, no. 6596383, pp. 891–895.Google Scholar
  3. 3.
    Odintsov, I.O., Professional’noe programmirovanie. Sistemnyi podkhod (Professional Programming: Systems Approach), St. Petersburg: BKhV-Peterburg, 2004.Google Scholar
  4. 4.
    McConnell, S., Code Complete: A Practical Handbook of Software Construction, Redmond, WA: Microsoft, 1993.Google Scholar
  5. 5.
    Cohn, M., Succeeding with Agile: Software Development Using Scrum, Upper Saddle River, NJ: Addison-Wesley, 2009.Google Scholar
  6. 6.
    Meyer, B., Agile! The Good, the Hype and the Ugly, NewYork: Springer-Verlag, 2014.Google Scholar
  7. 7.
    Maklakov, S.V., Modelirovanie biznes-protsessov s All-Fusion Process Modeler (BPwin 4.1) (Modeling of Business Processes with AllFusion Process Modeler (BPwin 4.1)), Moscow: Dialog, 2004.Google Scholar
  8. 8.
    Dubeikovskii, V.I., Effektivnoe modelirovanie s CA ERwin Process Modeler (BPwin; AllFusion Process Modeler) (Effective Modeling with CA ERwin Process Modeler (BPwin; AllFusion Process Modeler)), Moscow: Dialog, 2009.Google Scholar
  9. 9.
    Maklakov, S.V., Sozdanie informatsionnykh sistem s AlIFusion Modeling Suite (Creation of Information Systems with AlIFusion Modeling Suite), Moscow: Dialog, 2003.Google Scholar
  10. 10.
    Date, C.J., An Introduction to Database Systems, Boston, MA: Addison-Wesley, 2003, 8th ed.Google Scholar
  11. 11.
    Maklakov, S.V. and Tumanov, V.E., Proektirovanie relyatsionnykh khranilishch dannykh (Designing Relational Data Warehousing), Moscow: Dialog, 2007.Google Scholar
  12. 12.
    Hamilton, B., Ado. Net. Cookbook, Beijing: O’Reilly Media, 2003.Google Scholar
  13. 13.
    Troelsen, A., Pro C# 5.0 and the. NET 4.5 Framework, New York: Apress, 2012.CrossRefGoogle Scholar
  14. 14.
    Flenov, M.E., Bibliya C# (Bible C#), St. Petersburg: BKhV-Peterburg, 2011.Google Scholar
  15. 15.
    Larson, B., Delivering Business Intelligence with Microsoft SQL Server 2005: Utilize Microsoft’s Data Warehousing, Mining and Reporting Tools to Provide Critical Intelligence, New York: McGraw-Hill, 2006.Google Scholar
  16. 16.
    Larson, B., Microsoft SQL Server 2005 Reporting Services, New York: McGraw-Hill, 2006.Google Scholar
  17. 17.
    Spirin, N.A., Lavrov, V.V., Rybolovlev, V.Yu., et al., Model’nye sistemy podderzhki prinyatiya reshenii v ASU TP domennoi plavki metallurgii (Model Decision Support Systems in the Automated Process Control System of Blast Furnace Smelting in Metallurgy), Spirin, N.A., Ed., Yekaterinburg: Ural. Fed. Univ., 2011.Google Scholar
  18. 18.
    Onorin, O.P., Spirin, N.A., Terent’ev, V.L., et al., Komp’yuternye metody modelirovaniya domennogo protsessa (Computer Modeling the Blast Furnace Process), Spirin, N.A., Ed., Yekaterinburg: Ural. Gos. Tekh. Univ., 2005.Google Scholar
  19. 19.
    Spirin, N.A., Lavrov, V.V., Rybolovlev, V.Yu., et al., Matematicheskoe modelirovanie metallurgicheskikh protsessov v ASU TP: uchebnoe posobie (Mathematical Modeling of Metallurgical Processes in the Automated Process Control System: Manual), Spirin, N.A., Ed., Yekaterinburg: Ural. Fed. Univ., 2014.Google Scholar
  20. 20.
    Spirin, N.A., Ipatov, Yu.V., Lobanov, V.I., et al., Informatsionnye sistemy v metallurgii: uchebnik dlya vuzov (Information Systems in Metallurgy: Manual for Higher Education Institutions), Spirin, N.A., Ed., Yekaterinburg: Ural. Gos. Tekh. Univ., 2001.Google Scholar
  21. 21.
    Spirin, N.A., Lavrov, V.V., Rybolovlev, V.Y., Krasnobaev, A.V., and Pavlov, A.V., Use of contemporary information technology for analyzing the blast furnace process, Metallurgist, 2016, vol. 60, nos. 5–6. pp. 471–477.CrossRefGoogle Scholar

Copyright information

© Allerton Press, Inc. 2017

Authors and Affiliations

  • V. V. Lavrov
    • 1
  • N. A. Spirin
    • 1
  • I. A. Gurin
    • 1
  • V. Yu. Rybolovlev
    • 2
  • A. V. Krasnobaev
    • 2
  1. 1.Yeltsin Ural Federal UniversityYekaterinburgRussia
  2. 2.OAO Magnitogorskii Metallurgicheskii Kombinat (MMK)MagnitogorskRussia

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