Research and modeling of oil refining technological processes operating in the condition of stochastic uncertainty

Keywords: oil refinery, technological process, random quantity passive experiment, regression coefficients


As it is known, one of the initial and important stages in the creation of optimal control systems of oil refining technological units is the development of a mathematical model that can adequately record the processes at any time.

The operative and accurate measurement of all input and output variables is one of the important conditions in the development of a mathematical model of technological processes.

Studies have shown that the lack of information about the state of complex oil refining processes in many cases reduces their efficiency and effectiveness. On the other hand, the wide range of both quality and quantity of raw materials for processing makes their efficiency even more unsatisfactory. Under these conditions, it is difficult to develop mathematical models that can adequately describes the static modes of technological processes; the development of mathematical models is relevant both in scientific and practical terms.

A priori information required on input and output variables during normal operation of the technological complex in order to implement mathematical models identification for the vacuum block of the oil refining process unit is provided in the article. On the basis of this static information, mathematical dependencies were constructed between the variables characterizing the static mode of technological processes and the adequacy of the obtained mathematical models was confirmed through the statistical apparatus

In order to solve the problems, the research was determined to be able adequately describe the current technological conditions, which can quickly adapt to current technological situations and ensure the production of oil fractions with relatively stable quality, regardless of the disturbing effects of the system


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Author Biographies

Aygun Safarova, Azerbaijan State Oil and Industry University

Department of Electronics and Automation

Javida Damirova, Azerbaijan State Oil and Industry University

Department of Electronics and automation


Robinson, P. R.; Hsu, C.S., Robinson, P.R. (Eds.) (2007). Petroleum Processing Overview. Practical Advances in Petroleum Processing. New York: Springer, 1–78. doi:

Freedman, D. A. (2009). Statistical Models: Theory and Practice. Cambridge University Press, 459. doi:

Horst, A. M., Hill, A. P., Gorman, K. B. (2020). Palmerpenguins: Palmer Archipelago (Antarctica) penguin data. R package version 0.1.0. doi:

Golberg, M., Hokwon, C. (2010). Introduction to Regression Analysis. Edition: Revised and Updated EditionPublisher: Wessex Institute of Technology. WIT press. Available at:

Bukhtoyarov, V., Tynchenko, V., Petrovsky, E., Dokshanin, S., Kukartsev, V. (2019). Research of methods for design of regression models of oil and gas refinery technological units. IOP Conference Series: Materials Science and Engineerin, 537 (4). doi:

Ahmad, M. H., Adnan, R., Lau, C. K., Mohd Daud, Z. (2005). Comparing Least-Squares and Goal Programming Estimates of Linear Regression Parameter. MATEMATIKA: Malaysian Journal of Industrial and Applied Mathematics, 21, 101–112. Available at:

Grzelak, M., Borucka, A., Guzanek, P.; Machado, J., Soares, F., Trojanowska, J., Ivanov, V. (Eds.) (2022). Application of Linear Regression for Evaluation of Production Processes Effectiveness. Innovations in Industrial Engineering. Cham: Springer, 36–47. doi:

Derco, J., Černochová, L., Ivančinová, J., Lalai, A., Pijáková, I. (2012). Mathematical Modeling of an Oil Refinery WWTP. Polish Journal of Environmental Studies, 21 (4), 845–853. Available at:

Gut, A. (2005). Probability: A Graduate Course. Springer, 617. Available at:

Weiß, C., Puig, P.; Steland, A., Rafajłowicz, E.,Szajowski, K. (Eds.) (2015). The Marginal Distribution of Compound Poisson INAR(1). Springer Proceedings in Mathematics & Statistics. Springer, 351–359. doi:

Structural diagram of K-1 column as a modeling facility

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How to Cite
Safarova, A., & Damirova, J. (2022). Research and modeling of oil refining technological processes operating in the condition of stochastic uncertainty. EUREKA: Physics and Engineering, (5), 91-98.