Process Safety Management System based on Predictive Impulse Risk-Models


Annotation:

Presence of uncertainty in the knowledge about physical and chemical processes, as well as associated with the influence of random disturbances is one of the problems in the synthesis of industrial technology safety management systems. In this regard, there is a need to develop the new methods for the synthesis of process safety management systems, as well as improvement of the current systems. The main objectives of safety management systems are timely detection of failures and taking measures for eliminating their causes. At the same time, a promising approach for dynamic processes occurring in the weakly structured and poorly formalized environment is the application of methods based on the fundamental knowledge, implementation of target setting mechanisms, and revision of management quality criteria. When forming the technological processes safety management, it is proposed to use the risk criterion that reflects its level taking into account the influence of technological components uncertainty, and human risk factor. Nowadays, from a management point of view, the system of ensuring process safety is a multi-layered, hierarchically organized technological system that uses predictive control principles. Pulse signals are used for effective predictive management, and finite-difference automatic models — for situational management. The application of the proposed methods taking into account risk management is considered based on the example of the acetylene production process by oxidative pyrolysis of natural gas and the process of selective purification of tail gases of the unconcentrated nitric acid production. In this case, the risk-based management appeared to be favorable both in terms of saving all types of resources used and reducing the emissions of harmful and hazardous substances into the environment.

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DOI: 10.24000/0409-2961-2020-3-77-85
Year: 2020
Issue num: March
Keywords : process safety impulse risk-model intelligent agent multiagent system fuzzy sets
Authors:
  • Sanaeva G.N.
    Sanaeva G.N.
    Senior Lecturer Dmitry Mendeleev University of Chemical Technology of Russia, Novomoskovsk, Russia
  • Prorokov A.E.
    Prorokov A.E.
    Cand. Sci. (Eng.), Assoc. Prof. Dmitry Mendeleev University of Chemical Technology of Russia, Novomoskovsk, Russia
  • Vent D.P.
    Vent D.P.
    Dr. Sci. (Eng.), Prof. Dmitry Mendeleev University of Chemical Technology of Russia, Novomoskovsk, Russia
  • Vinogradov G.P.
    Vinogradov G.P.
    Dr. Sci. (Eng.), Prof. Tver State Technical University, Tver, Russia
  • Bogatikov V.N.
    Bogatikov V.N.
    Dr. Sci. (Eng.), Prof., vnbgtk@mail.ru Tver State Technical University, Tver, Russia