Abstract
This article analyzes the problems of intelligent control of the process of low-temperature separation (LTS) of natural gas and modern approaches to solving them. Since the process complexity of gas separation at low temperature as well as multidimensional technological parameters have nonlinear control properties, a principled technological scheme for intellectual control of technological parameters such as gas humidity, temperature, pressure, consumption and diethylenglicol (DEG) consumption of the LTS process has been developed. The analysis and literature review show that intellectual control methods allow for stable operation of the technological process, reduced energy consumption, reduced heat exchange, and reduced gas hydrate formation in low-temperature separation devices. This study presents the conclusions obtained from the implementation of adaptive and intelligent control systems in low-temperature gas separation processes, as well as the existing scientific problems.
First Page
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Last Page
92
References
1. Ibragimova, A.T., Mezentseva, T.A. (2023). Dynamic modeling of the low-temperature separation process to determine the unit’s throughput capacity. Exposition Oil Gas. 8. 94–98. doi: 10.24412/2076-6785-2023-8-94-98.
2. Taxumi, N., Hasan, M., Smit, R. (2009). Optimization of Integrated Low-Temperature Gas Separation Processes Using SA Method and Different Refrigerants. Iranian Journal of Chemical Engineering. 6(4).
3. Kravtsov, A.V., Usheva, N.V., Moises, O.E., Kuzmenko, E.A., Anufrieva, O.V. (2009). Analysis of the influence of technological parameters and optimization of low-temperature separation processes. Proceedings of Tomsk Polytechnic University. 315(3). 57–60.
4. Dmytrenko, V., Podoliak, T. (2024). Research of methanol content in technological flows of facilities that process gas preparation by low-temperature separation method. Technology Audit and Production Reserves. 6. 1(80). 46–53. doi: 10.15587/2706-5448.2024.318926.
5. Zemenkova, M.Yu., Chizhevskaya, E.L., Zemenkov, Yu.D. (2022). Intellectual monitoring of the state of hydrocarbon pipeline transport facilities using neural network technologies. Mining Institute Notes. 258. 933–944. doi: 10.31897/PMI.2022.105. (in Russian).
6. Qazi, N., Yeung, H. (2014). Modeling of gas-liquid separation through stacked neural network. Asia-Pacific Journal of Chemical Engineering. 9. 490–497. doi: 10.1002/apj.1777.
7. Wang, J.N., Smith, R. (2005). Synthesis and optimization of low-temperature gas separation processes. Industrial and Engineering Chemistry Research. 44(8). 2856–2870. doi: 10.1021/ie0496131.
8. Dolganov, I.M., Pisarev, M.O., Ivashkina, E.N., Dolganova, I.O. (2014). Operating modes of the gas and gas condensate preparation unit in modeling low-temperature separation technology. Oil and Coal. 56(3). 187–206. doi: 10.17122/ogbus-2014-3-187-206. (in Russian).
9. Horbiychuk, M., Jankak, I., Skripka, A. (2023). Mathematical modeling of the low-temperature separation process. Modern Engineering and Innovative Technologies. 1(27-01). 101–122. doi: 10.30890/2567-5273.2023-27-01-009.
10. Zadeh, L.A. (1965). Fuzzy sets. Information and Control. 8(3). 338–353.
11. Qin, S.J., Badgwell, T.A. (2003). A survey of industrial model predictive control technology. Control Engineering Practice. 11(7). 733–764.
12. Boryn, V.S., Sahai, I.M. (2018). Intelligent system of automatic control over the technological process of natural gas preparation based on neural networks. Oil and Gas Power Engineering. 1(29). 89–98.
13. Hadian, M., Jahed-Motlagh, R., Khayatian, A.R. (2015). Neural network predictive controller for gas distribution networks. IFAC-PapersOnLine. 48(30). 436–441.
14. Moetamedzadeh, H.R., Jahed-Motlagh, M.R., Hadian, M. (2020). Intelligent nonlinear model predictive control of gas pipeline networks. Transactions of the Institute of Measurement and Control. 42(5). 943–956.
15. Horbiychuk, M., Pobihun, V., Slabyi, O. (2023). Mathematical modeling of the low-temperature separation process of natural gas. Modern Engineering and Innovative Technologies. 27. 45–53.
16. Surmi, A., Ahmadi, M.A., Bahadori, M. (2023). Modeling of nitrogen removal from natural gas using artificial neural networks. Chemical Engineering Research and Design. 197. 120–132.
17. Arabsky, A.K., Zavyalov, S.V., Efimov, A.N., et al. (2019). A method for automatically controlling the productivity of a low-temperature gas separation unit. Patent RU2709045C1. No. 2019100287. Published December 13, 2019. Bulletin No. 35. 16 p.
18. Arno, O.B., Arabskiy, A.K., Zavyalov, S.V., et al. (2019). Method for automatic control of the productivity of a low-temperature gas separation unit in extreme northern conditions. Patent RU2709044S1. Published December 13, 2019. Bulletin No. 35. 15 p.
19. Arno, O.B., Arabskiy, A.K., Ageev, A.L., et al. (2022). Method for automatic control of a low-temperature gas separation unit operating in the conditions of the Russian Federation far north. Patent RU2782988C1. Published November 8, 2022. Bulletin No. 31. 19 p.
Recommended Citation
Amirov, Shaxzod Nurmuhammad o‘g‘li
(2026)
"INVESTIGATION OF THE PROBLEMS OF INTELLIGENT CONTROL OF THE PROCESS OF LOW-TEMPERATURE SEPARATION OF NATURAL GAS.,"
Chemical Technology, Control and Management: Vol. 2026:
Iss.
3, Article 11.
DOI: https://doi.org/10.59048/2181-1105.1814
