Industrial and Systems Engineering | Article | Published 2019-11-24

FUZZY-SITUATIONAL DIAGNOSTICS OF TECHNOLOGICAL SAFETY OF PETROCHEMICAL PLANTS

Authors:
Publisher: International Journal of Engineering Applied Sciences and Technology
Collection: International Journal of Engineering Applied Sciences and Technology
DOI: 10.33564/IJEAST.2019.V04I07.030
Keywords: Information networks, diagnostics, process safety, soft computing, fuzzy set theory, fuzzy logic, fuzzy model, fuzzy decision making, complex industrial facilities

Abstract

The article will discuss the issues of fuzzy modeling and it will provide the analysis of technology-related situations for ensuring the safety of complex industrial facilities in line with the decision-making process of their management. On the basis of theory of fuzzy sets and fuzzy logic, the process of making decisions under the conditions of uncertainty and uncertainty of the initial information will be studied. In addition, the study will look at the problems in diagnostics and control of technical safety at chemical plants and chemical industry, in general. It has been proven that the diagnostic system of chemical processes lies at the core of the modern safety process and control system of chemical production. A great deal of research has been done on a number of general features within the creation and use of aforementioned diagnostic systems in chemical processes. The research has employed the formal method in which the functioning dynamics of petrochemical facilities is analyzed based on the theory of fuzzy sets and fuzzy logic. This method allows to develop a set of measures aimed at managing the technical safety of petrochemical facilities and, consequently, the reduction of loss and the increase of efficiency among the personnel. This the efficiency of service personnel can be achieved by improving the state of performance and predicting technology-related failures within equipment and control systems.

References

  1. [1] V. S. Simankov, A.V. Shopin, "Situational
  2. Control of a Complex Object in the Conditions
  3. of Fuzzy Initial Information", Krasnodar works
  4. of FORA. № 9. Pp.116-120, 2004.
  5. [2] D.P. Vent, A.E. Prophets, N.A. Toichkin,
  6. "Mathematical models used in diagnostics of
  7. technological systems". № 3, Pp. 1-20. 2015.
  8. [3] I.H. Sidikov, D. Umurzakova, Adaptive Neurofuzzy Regulating System of the temperature Mode of the Drum Boiler, IJARSET, Volume 6,
  9. Issue 1, January, Pp. 7869-7873, 2019.
  10. [4] A.F. Egorov, T.V. Savitckaya, "Methods and
  11. models of risk analysis and safety chemical
  12. technology control", Basis of theory chemical
  13. technology. Тоm 44, №3. Pp.341-353, 2010.
  14. [5] I.H. Sidikov, D. Umurzakova, Mathematical
  15. Modeling of Transient Processes of the
  16. Automatic Control System of Water Level in the
  17. Steam Generator. UJME., 7(4): DOI:
  18. 10.13189/ujme.2019.070401.pp. 139-146, 2019.
  19. [6] N.G. Yarushkina, "Basis of theory fuzzy and
  20. hybrid systems". Moscow: Publishing house
  21. «Financial and statistic». Pp.– 320, 2004.
  22. [7] R.A. Aliyev, R.R. Aliyev, "Theory of intellectual
  23. systems". Baku: Publishing house «Chashiogly»,
  24. Pp. –720, 2001.
  25. [8] A.V. Аndreychikov, O.N. Аndreychikova,
  26. "Intellectual information systems", Moscow:
  27. Publishing house «Financial and statistic».
  28. Pp.2004. – 424.
  29. [9] A.A. Uskov, "Intellectual technology of control.
  30. Artificial neural network and fuzzy logic".
  31. Moscow: Publishing house «Hot line - telekom».
  32. Pp. 2004. -143.
  33. [10] A. Riid, Transparent Fuzzy systems: Modeling
  34. and Control. Pp. 2002. -227.
  35. [11] Deng Yong, Shi Wenkang, A modified
  36. aggregation of fuzzy opinions under group
  37. decision making. // J.of Computers and Systems
  38. Sciences International. V. 42. 2003.
  39. [12] Z.Wang, R.Yang, L.Wang, Intelligent Multiagent Control for Integrated Building and Microgrid Systems. // IEEE PES Innovative Smart
  40. Grid Technologies (ISGT), Pp. 1-7, 2011.
  41. [13] A. Celikyilmaz, I.B. Turksen, Enhanced fuzzy
  42. system models with improved fuzzy clustering
  43. algorithm. // IEEE Trans. Fuzzy Systems, Vol.
  44. 16, Pp. 779-794, 2008.
  45. [14] Kazuo Tanaka, Hua O. Wang, "Fuzzy Control
  46. Systems Design and Analysis", Linear Matrix
  47. Inequality Approach / Copyright. – New York:
  48. John Wiley & Sons, Inc., Pp.– 305, 2001.
  49. [15] William Siler, James J. Buckley, "Fuzzy expert
  50. systems and fuzzy reasoning." Hoboken, NewJersey: John Wiley & Sons, Inc., Pp. 405, 2005.
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