Automated process control system for obtaining elemental sulfur based on risk assessment

We consider this type of management objects, the functioning of which occurs under various types of uncertainty and requires the development of a specific approach to management, analysis and evaluation of a variety of factors that affect the work and are not amenable to quantitative and qualitative description. In this paper, the task of controlling the technological process of obtaining sulfur is formulated. The analysis of the technological installation for obtaining elemental sulfur by the Claus method as a control object is carried out. Risk factors for the process under consideration are highlighted. A model for identifying the current state of the control object is proposed. The algorithm of management intellectualization, which acts as a base for the developed expert subsystem, is synthesized. The structure and order of functional interaction of the expert subsystem of the automated process control system with the decision-maker is given. The author developed an algorithmic support for the expert subsystem of the automated control system for the technological process of sulfur production, designed for integration into the operating environment of the operator's automated workplace. The efficiency of the synthesized expert subsystem is evaluated by using a training simulator that simulates the operation of the control panel of the process unit for obtaining elemental sulfur using the Claus method. Based on the results of modeling 35 emergencies, 100% were identified by the expert subsystem, of which 28% were prevented at an early stage by implementing the generated recommendations.

1. Protalinskii O.M., Azhmukhamedov I.M.. Sistemnyi analiz i modelirovanie slabo strukturirovannykh i plokho formalizuemykh protsessov v sotsiotekhnicheskikh sistemakh. Inženernyj vestnik Dona (Rus). 2012;3. Available at: http://www.ivdon.ru/ru/magazine/archive/n3y2012/916 (In Russ) (accessed: 20.08.2020).

2. Pechenkin D.V. Sistema otsenki riska pri ehkspluatatsii tekhnologicheskikh ustanovok polucheniya ehlementarnoi sery metodom Klausa. Systems. Methods. Technologies. 2018;1(37):72-78. (In Russ)

3. Protalinskii O. M., Shcherbatov I. A. Programmnyi kompleks dlya obucheniya operatorov tekhnologicheskogo protsessa polucheniya sery. Izvestiya vysshikh uchebnykh zavedenii. Severo-Kavkazskii region. Tekhnicheskie nauki. 2006;2:29-34. (In Russ)

4. Frey W. Socio-Technical Systems in Professional Decision Making. Available at: http://cnx.org/content/m14025/latest/ (accessed: 20.08.2020).

5. Fishburn P. Utility Theory for Decision-Making. N.Y., Wiley, 1970, 234 p.

6. Rotshtein A.P., Shtovba S.D. Vliyanie metodov defazzifikatsii na skorost' nastroiki nechetkoi modeli .Kibernetika i sistemnyi analiz. 2002;5:169-176. (In Russ)

7. Shcherbatov I.A. Klassifikatsiya neopredelennostei v zadachakh modelirovaniya i upravleniya slozhnymi slaboformalizuemymi sistemami. Vestnik Saratovskogo gosudarstvennogo tekhnicheskogo universiteta. 2013;1(1):175-179. (In Russ)

8. Protalinskii O.M., Shcherbatov I.A., Esaulenko V.N.. Analysis and Modelling of Complex Engineering Systems Based on the Component Approach. World Applied Sciences Journal. 2013;24(2):276-283.

9. Matveikin V.G., Dmitrievskii B.S., Popov N.S., Dmitrieva O.V. Integrirovannaya model' innovatsionno-proizvodstvennoi sistemy. Vestnik Tambov. gos. tekhn. un-ta. 2016;22(4):550-558. (In Russ)

10. Matveikin V.G., Dmitrievskii B.S., Panchenko I.S., Kokoreva M.V. Sistemy dispetcherizatsii i upravleniya: uchebnoe posobie. Tambov: Izd-vo FGBOU VPO “TGTU”, 2013. - 96 p. (In Russ)

11. Lvovich Y.E., Pitolin A.V., Sapozhnikov G.P. Multi-method approach to the modeling of complex systems based on monitoring data analysis. Modeling, Optimization and Information Technology. 2019;7(2):301-310. (In Russ)