DECISION SUPPORT SYSTEM WITH INTELLECTUALIZATION ELEMENTS FOR MANAGING THE PETROLEUM COKE PRODUCTION PROCES

In this study, a decision support system with intellectualization elements was created to control the petroleum coke production process based on the developed models of the main units of the delayed coking unit (УЗК) taking into account fuzzy information from experts. A description of the main interfaces of the created intelligent decision support system (IDSS) is given, with the help of which the user of the system models various operating modes of the main УЗК units, optimizes their parameters and effectively controls the coke production process. Using the created IDSS, the user can control the coke production process according to various criteria depending on the demand for volume and requirements for coke quality. Using the IDSS, the user of the system can maximize the volumes of coke produced with the required quality indicators or provide the required volume of coke with the best quality indicators. Unlike other systems based on known deterministic УЗК models, the created IDSS allows more adequate modeling, effective optimization and control of УЗК operating modes. Based on the comparison of the simulation results for optimizing the УЗК operating modes using the created ISPR, known systems of deterministic simulation and optimization, the advantages of the created system in comparison with known similar systems were established.

1. Cao L. Experimental Study of Petroleum Coke Ash in Concrete Applications / L. Cao // Chem. Technol. Fuels Oils. Innovative technologies of oil and gas. – 2025. – Vol. 61. – P. 280-290. https://doi.org/10.1007/s10553-025-01860-9. (In English).

2. Suchkov S.I. Hearth Gasification Using Petroleum Coke in Power Production / S.I. Suchkov // Power Technol. Eng. – 2021. – Vol. 55. – P. 600-606. https://doi.org/10.1007/s10749-021-01404-1. (In English).

3. Tuleuov Zh.N. Technological regulations for the delayed coking unit УЗК 21–10/6 of the Atyrau Oil Refinery / Zh.N. Tuleuov. – Atyrau, 2020. – 220 p. (In Russian).

4. Methods for Modeling and Optimizing the Delayed Coking Process in a Fuzzy Environment / B.Orazbayev et al // Processes. – 2023. – Vol. 1, № 2. – P. 1-19. https://doi.org/10.3390/pr11020450. (In English).

5. Meikle T. Design and development of a decision support system to support discretion in refugee law / T. Meikle, J. Yearwood // Research and Development in Intelligent Systems XVII – London: Springer, 2018. – P. 243-256. https://doi.org/10.1007/978-1-4471-0269-4_18. (In English).

6. Yadav M. Development of a Decision Support System for Fixture Design / M. Yadav, S. Mohite // Technology Systems and Management. Communications in Computer and Information Science. – Berlin, Heidelberg: Springer, 2021. – Vol. 145. – P. 182-189. https://doi.org/10.1007/978-3-642-20209-4_25. (In English).

7. Arnott D. A critical analysis of decision support systems research / D. Arnott, G. Pervan // Journal of Information Technology. – 2018. – Vol. 20, № 2. – P. 67-87. https://doi.org/10.1057/palgrave.jit.2000035. (In English).

8. Pryor T.A. Development of Decision Support Systems // Decision Support Systems in Critical Care. Computers and Medicine / M.M. Shabot, R.M. Gardner (eds). – New York: Springer, 2020. – P. 61-73. https://doi.org/10.1007/978-1-4612-2698-7_4. (In English).

9. Arinze B. Decision Support Systems (DSS) development using a model of user inquiry types: Methodological proposals and a case study / B. Arinze // Systems Practice. – 2022. – Vol. 5. – P. 629-650. (In English).

10. Jatinder N.D. Intelligent Decision-making Support Systems / N.D. Jatinder, A.F. Guisseppi, M.T. Manuel; London: Springer, 2019. – 503 p. https://doi.org/10.1007/1-84628-231-4. (In English).

11. Gachet A. Development Processes of Intelligent Decision-making Support Systems: Review and Perspective / A. Gachet, P. Haettenschwiler // Intelligent Decision-making Support Systems. Decision Engineering. – London: Springer, 2018. – P. 97-121. https://doi.org/10.1007/1-84628-231-4_6. (In English).

12. Design and Development of an Intelligent Decision Support System Applied to the Diagnosis of Patients Susceptible to Heart Failure / A. Álvarez-Pazó et al // Proc. of TEEM 2023. Lecture Notes in Educational Technology. – Singapore: Springer, 2024. – P. 280-288. https://doi.org/10.1007/978-981-97-1814-6_27. (In English).

13. Bazilevych K.O. Intelligent Decision-Support System for Epidemiological Diagnostics. II. Information Technologies Development / K.O. Bazilevych, D.I. Chumachenko, L.F. Hulianytskyi // Cybern. Syst. Anal. – 2022. – Vol. 58. – P. 499-509. https://doi.org/10.1007/s10559-022-00484-9. (In English).

14. Arvind K. Passive and active experimental methods / K. Arvind, P. Bernhard // 1th Int. Conf. PAM 2010, Zurich, Switzerland, Proceedings. – Springer, 2019. – P. 7-15. https://doi.org/10.1007/978-3-642-12334-4. (In English).

15. Bronshtein I.N. Probability theory and mathematical statistics / I.N. Bronshtein, K.A. Semendyayev // Handbook of Mathematics. – Berlin, Heidelberg: Springer, 2019. – P. 598-635. https://doi.org/10.1007/978-3-662-25651-0_5. (In English).

16. Krivonogov A.A. Application of the Method of Expert Assessments in Determining the Level of Criticality of the Information Security Vulnerability of Smart Contracts / A.A. Krivonogov, Y.N. Philippovich, S.A. Kesel // Software Engineering Application in Systems Design. Lecture Notes in Networks and Systems. – 2023. – vol 596. – P. 512-530. https://doi.org/10.1007/978-3-031-21435-6_45. (In English).

17. Methods Based on Fuzzy Set Theory / J.N. Mordeson et al // Linear Models in the Mathematics of Uncertainty. Studies in Computational Intelligence. – 2022. – vol. 463. – P. 163-175. https://doi.org/10.1007/978-3-642-35224-9_10. (In English).

18. Tarczyński W. Selected Statistical Methods in Experimental Studies / W. Tarczyński // Selected Issues in Experimental Economics. Springer Proceedings in Business and Economics / Nermend K., Łatuszyńska M. (eds). – Cham: Springer, 2021. – P. 91-106. https://doi.org/10.1007/978-3-319-28419-4_7. (In English).

19. Yazdi M. Linguistic Methods Under Fuzzy Information in System Safety and Reliability Analysis / M. Yazdi; Cham: Springer, 2022. – 200 p. https://doi.org/10.1007/978-3-030-93352-4. (In English).

20. The concept of creating intelligent decision support for managing the operating modes of chemical engineering systems / B.B. Orazbayev et al; Astana: AS Innovation Publishing House, Eurasian National University, 2022. – 391 p. (In Russian).

21. Development of Coke Chambers Models of Delayed Coking Unit under uncertain initial information / В. Assanova et al // Proc. 7th Int. Symp. on Innovative Approaches in Smart Technologies (ISAS). – Istanbul, Turkiye, 2023. – P. 1-5. https://doi.org/10.1109/ISAS60782.2023.10391778. (In English).

22. A fuzzy decision-making method for controlling operation modes of a hard-to-formalise rectification column of a delayed coking unit / B. Assanova et al // News of the National Academy of Sciences of the Republic of Kazakhstan, Series of Geology and Technical Sciences. – 2024. – № 1. – P. 17-30. https://doi.org/10.32014/2024.2518-170X.362. (In English).

23. Shumsky V.M. Engineering Tasks in Oil Refining and Petrochemistry. 2nd ed. / V.M. Shumsky, L.A. Zyryanova; Moscow: MPC Publication, 2023. – 375 p. (In Russian).