Development of mathematical models and optimization of operation modes of the oil heating station of main oil pipelines under conditions of fuzzy initial information

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.244949

Keywords:

mathematical models, optimization, multi-criteria selection, main oil pipelines, oil heating station, fuzzy information, decision-maker, heuristic method

Abstract

The relevance of the study is substantiated by the fact that when managing the processes of oil transportation through main pipelines, it becomes necessary to determine and select the optimal operating modes of the oil pipeline units, taking into account the fuzziness of some part of the initial information. In this regard, solving the problem of multi-criteria selection of effective operating modes for an oil heating station for a hot oil pipeline system, which is often described in a fuzzy environment, based on the apparatus of fuzzy set theories, is an urgent scientific and practical problem. A method for the synthesis of models in the conditions of fuzzy output parameters of the object has been developed, with the help of which fuzzy models of the investigated oil heating station of the main oil pipeline have been built. Based on the modification and combination of various optimality principles, mathematical formulations of the problem of multi-criteria selection of effective operating modes for an oil heating station in a fuzzy environment are obtained. By modifying and adapting the principles of guaranteed results and equality in a fuzzy environment, a heuristic method has been developed for solving the formulated problem of selecting object's operation modes using the initial fuzzy information. The proposed heuristic method for multi-criteria selection in a fuzzy environment is based on the use of the experience and knowledge of the decision-maker. The proposed approach is implemented in the formulation and solution of the problem of multi-criteria selection of operating modes of the oil heating station in Atyrau of the Uzen-Atyrau-Samara main oil pipeline. As a result of the application of the proposed method, an improvement in the degree of fulfillment of a fuzzy restriction on environmental impact was achieved by 2 %, as well as the optimal values of the operating parameters of the object were improved: the temperature was reduced by 1.85 % (5.67 K), pressure – by 0.04 % (kPa) and fuel consumption – by 2.9 % (0.0002 kg/s). The obtained results have confirmed the effectiveness of the proposed approach to solving the assigned tasks.

Supporting Agency

  • We are grateful to Zhasulan Tuleuov, Yerbol Tulegenov and Bakytzhan Bultekov, who provided very important information for this study. The research data was sponsored by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant No.of the research fund AP08855680–Intelligent decision support system for managing the operating modes of a catalytic reforming unit).

Author Biographies

Batyr Orazbayev, L. N. Gumilyov Eurasian National University

Doctor of Technical Sciences, Professor

Department of System Analysis and Control

Zhadra Moldasheva, L. N. Gumilyov Eurasian National University

Doctoral Student

Department of Information Systems

Kulman Orazbayeva, Kazakh University of Economics, Finance and International Trade

Doctor of Technical Sciences, Professor

Department of Management

Valentina Makhatova, Dosmukhamedov Atyrau University

PhD, Professor

Department of Software Engineering

Lyailya Kurmangaziyeva, Dosmukhamedov Atyrau University

PhD, Associate Professor

Department of Software Engineering

Aigul Gabdulova, Dosmukhamedov Atyrau University

Master, Senior Lecturer

Department of Software Engineering

References

  1. Lur'e, M. V. (2012). Matematicheskoe modelirovanie processov truboprovodnogo transporta nefti, nefteproduktov i gaza. Moscow: RGU nefti i gaza im. I.M. Gubkina, 456. Available at: https://www.twirpx.com/file/2089034/
  2. Seleznev, V. E., Pryalov, S. N. (2018). Metody postroeniya modeley techeniy v magistral'nyh truboprovodah. Moscow: Editorial, 560. Available at: https://rutracker.org/forum/viewtopic.php?t=4337958
  3. Vaynshtok, S. M. (2015). Truboprovodniy transport nefti. Moscow: OOO «Nedra-Biznescentr», 407.
  4. Gucykova, S. (2011). Metod ekspertnyh ocenok. Teoriya i praktika. Moscow: Kogito-Centr, 144. Available at: https://www.labirint.ru/books/295292/
  5. Brovkin, L. A., Korotin, A. N., Krylov, L. V. et. al. (2017). Matematicheskoe modelirovanie i proektirovanie promyshlennyh pechey. Ivanova: IGU, 358.
  6. Arutyanov, V. A., Buhmirov, V. V., Krupenikov, S. A. (2012). Matematicheskoe modelirovanie teplovoy raboty promyshlennyh pechey. Moscow: Metallurgiya, 245. Available at: https://www.twirpx.com/file/645128/
  7. Adel'son, S. V., Bavshin, C. A. (2015). Trubchatye pechi s izluchayuschimi stenkami topki. Moscow: GOSINTI, 196.
  8. Ziganshin, G. K. (2010). Tehnologicheskiy raschet trubchatoy pechi na EVM. Ufa: Izd. UGNTU, 100.
  9. Tausheva, E. V., Taushev, V. V., Telyashev, E. G. (2012). Pat. No. 2483096 RU. Trubchataya pech'. declareted: 07.02.2012; published: 27.05.2013.
  10. Rodin, A. A. (2017). Optimizaciya transporta vysokovyazkih neftey s podogrevom i primeneniem uglevodorodnyh razbaviteley. Moscow, 28.
  11. Arslanov, A. A. (2017). Matematicheskie modeli trubchatyh pechey. Moscow, 147.
  12. Zamani Sabzi, H., King, J. P., Abudu, S. (2017). Developing an intelligent expert system for streamflow prediction, integrated in a dynamic decision support system for managing multiple reservoirs: A case study. Expert Systems with Applications, 83, 145–163. doi: https://doi.org/10.1016/j.eswa.2017.04.039
  13. Dubois, D. (2011). The role of fuzzy sets in decision sciences: Old techniques and new directions. Fuzzy Sets and Systems, 184 (1), 3–28. doi: https://doi.org/10.1016/j.fss.2011.06.003
  14. Suleymenov, B. A., ZHunisbekov, M. Sh., Sugurova, L. A., Suleymenov, A. B. (2014). Intellektual'nye i gibridnye sistemy upravleniya tehnologicheskimi processami: teoriya, metody i prilozheniya. Vestnik nauki Kostanayskogo social'no-tehnicheskogo universiteta imeni akademika Zulharnay Aldamzhar. Available at: https://articlekz.com/article/28692
  15. Ryzhov, A. P. (2017). Teoriya nechetkih mnozhestv i ee prilozheniy. Moscow: Izd-vo MGU, 115.
  16. Pavlov, S. Y., Kulov, N. N., Kerimov, R. M. (2014). Improvement of chemical engineering processes using systems analysis. Theoretical Foundations of Chemical Engineering, 48 (2), 117–126. doi: https://doi.org/10.1134/s0040579514020109
  17. Reverberi, A. P., Kuznetsov, N. T., Meshalkin, V. P., Salerno, M., Fabiano, B. (2016). Systematical analysis of chemical methods in metal nanoparticles synthesis. Theoretical Foundations of Chemical Engineering, 50 (1), 59–66. doi: https://doi.org/10.1134/s0040579516010127
  18. Kenzhebaeva, T. S., Orazbayev, B. B., Abitova, G. A., Orazbayeva, K. N., Spichak, Y. V. (2017). Study and design of mathematical models for chemical-technological systems under conditions of uncertainty based on the system analysis. Proceedings of the 2017 International Conference on Industrial Engineering and Operations Management (IEOM), 776–790. Available at: http://www.ieomsociety.org/ieomuk/papers/183.pdf
  19. Gmurman, V. E. (2016). Teoriya veroyatnostey i matematicheskaya statistika. Moscow: Vysshee obrazovanie, 479. Available at: https://urait.ru/book/teoriya-veroyatnostey-i-matematicheskaya-statistika-370815
  20. Zhao, Z.-W., Wang, D.-H. (2012). Statistical inference for generalized random coefficient autoregressive model. Mathematical and Computer Modelling, 56 (7-8), 152–166. doi: https://doi.org/10.1016/j.mcm.2011.12.002
  21. Karmanov, F. I., Ostreykovskiy, V. A. (2019). Statisticheskie metody obrabotki eksperimental'nyh dannyh s ispol'zovaniem paketa MathCad. Moscow: Infra-M, 208. Available at: https://znanium.com/catalog/document?id=355561
  22. Orazbayev, B., Ospanov, E., Kissikova, N., Mukataev, N., Orazbayeva, K. (2017). Decision-making in the fuzzy environment on the basis of various compromise schemes. Procedia Computer Science, 120, 945–952. doi: https://doi.org/10.1016/j.procs.2017.11.330
  23. Biegler, L. T., Lang, Y., Lin, W. (2014). Multi-scale optimization for process systems engineering. Computers & Chemical Engineering, 60, 17–30. doi: https://doi.org/10.1016/j.compchemeng.2013.07.009
  24. Chen, Y., He, L., Li, J., Zhang, S. (2018). Multi-criteria design of shale-gas-water supply chains and production systems towards optimal life cycle economics and greenhouse gas emissions under uncertainty. Computers & Chemical Engineering, 109, 216–235. doi: https://doi.org/10.1016/j.compchemeng.2017.11.014
  25. Yudin, D. B. (1989). Vychislitel'nye metody teorii prinyatiya resheniy. Moscow: Nauka, 320. Available at: https://www.twirpx.com/file/102264/
  26. Orazbayev, B. B., Shangitova, Z. Y., Orazbayeva, K. N., Serimbetov, B. A., Shagayeva, A. B. (2020). Studying the Dependence of the Performance Efficiency of a Claus Reactor on Technological Factors with the Quality Evaluation of Sulfur on the Basis of Fuzzy Information. Theoretical Foundations of Chemical Engineering, 54 (6), 1235–1241. doi: https://doi.org/10.1134/s0040579520060093
  27. Valeev, S. G. (1991). Regression modelling in observations treatment. Мoscow: Nauka, 272. Available at: https://www.elibrary.ru/item.asp?id=25354868
  28. Fuzzy Logic Toolbox. Available at: https://www.mathworks.com/help/fuzzy/
  29. Orazbayev, B., Ospanov, Y., Orazbayeva, K., Makhatova, V., Kurmangaziyeva, L., Utenova, B. et. al.; Orazbayev, B., Ospanov, Y. (Eds.) (2021). System concept for modelling of technological systems and decision making in their management. Kharkiv: РС ТЕСHNOLOGY СЕNTЕR, 180. doi: https://doi.org/10.15587/978-617-7319-34-3
  30. Orazbayev, B. B., Orazbayeva, K. N., Utenova, B. E., Shagayeva, A. B., Kassimova, B. R. (2020). Multi-criteria selection of operating modes of main pipeline units during oil transportation with fuzzy information. Bulletin of the Tomsk Polytechnic University, 331 (12), 105–116. doi: https://doi.org/10.18799/24131830/2020/12/2944
  31. Orazbayev, B., Assanova, B., Bakiyev, M., Krawczyk, J., Orazbayeva, K. (2020). Methods of model synthesis and multi-criteria optimization of chemical-engineering systems in the fuzzy environment. Journal of Theoretical and Applied Information Technology, 98 (6), 1021–1036. Available at: https://www.scopus.com/record/display.uri?eid=2-s2.0-85083790104&origin=resultslist
  32. Orazbayev, B. B., Ospanov, E. A., Orazbayeva, K. N., Kurmangazieva, L. T. (2018). A Hybrid Method for the Development of Mathematical Models of a Chemical Engineering System in Ambiguous Conditions. Mathematical Models and Computer Simulations, 10 (6), 748–758. doi: https://doi.org/10.1134/s2070048219010125
  33. Bogdanov, R. M. (2014). Program complex for modelling of operation of the main pipelines. Oil and Gas Business, 1, 166–177. doi: https://doi.org/10.17122/ogbus-2014-1-166-177
  34. Bogdanov, R. M., Lukin, S. V. (2010). Pat. No. 2011611173 RU. Opredelenie ryada optimal'nyh rezhimov raboty magistral'nyh truboprovodov pri vybrannyh kriteriyah optimal'nosti (ORORMT)». Zaregistr. v Reestre programm dlya EVM po zayavke No. 2010617845 ot 4.02.2011 g.
  35. Gol'yanov, A. I., Gol'yanov, A. A., Kutukov, S. E. (2017). Obzor metodov ocenki energoeffektivnosti magistral'nyh nefteprovodov. Problemy sbora, podgotovki i transporta nefti i nefteproduktov, 4 (110), 156–170.
  36. Gol'yanov, A. I., Gol'yanov, A. A. (2016). Faktory, vliyayuschie na energoeffektivnost' raboty nefteprovodov. Truboprovodnyy transport – 2016: Materialy XI Mezhdunarodnoy uchebno-nauchno-prakticheskoy konferencii. Ufa: Izd-vo UGNTU, 47–49.
  37. Grossmann, I. E. (2014). Challenges in the application of mathematical programming in the enterprise-wide optimization of process industries. Theoretical Foundations of Chemical Engineering, 48 (5), 555–573. doi: https://doi.org/10.1134/s0040579514050182
  38. Novak, N., Louli, V., Skouras, S., Voutsas, E. (2018). Prediction of dew points and liquid dropouts of gas condensate mixtures. Fluid Phase Equilibria, 457, 62–73. doi: https://doi.org/10.1016/j.fluid.2017.10.024
  39. Afanas'ev, I. A., Gaysin, E. Sh., Frolov, Yu. A. (2018). Modelirovanie migracii nefti (nefteproduktov) pri avariynyh razlivah na magistral'nyh truboprovodah. Truboprovodniy transport – 2018: tezisy dokladov XIII Mezhdunarodnoy uchebno-nauchno-prakticheskoy konferencii. Ufa, 11–13. Available at: https://rusoil.net/files/2018-05/Sbornik-tez-Truboprov-transport-2018.pdf
  40. Baronets, V. D., Grechikhin, M. A. (1992). A model for representing the membership function in expert systems. Automation and Remote Control, 53 (6), 921–925.
  41. Valiakhmetov, R. I., Yamaliev, V. U., Shubin, S. S., Alferov, A. V. (2018). Application of heuristic algorithms in analyzing data to solve the problem of detection of electric centrifugal pumping units. Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering, 329 (2), 159–167. Available at: http://earchive.tpu.ru/bitstream/11683/46406/1/bulletin_tpu-2018-v329-i2-15.pdf
  42. Tussupov, J., La, L., Mukhanova, A. (2014). A model of fuzzy synthetic evaluation method realized by a neural network. International Journal of Mathematical Models and Methods in Applied Sciences, 8, 103–106. Available at: https://www.researchgate.net/publication/295492008_A_model_of_fuzzy_synthetic_evaluation_method_realized_by_a_neural_network
  43. Pershin, Yu. (1994). Pareto-optimal and lexicographic solutions of mixed-integer problems that are linear with respect to continuous variables. Automation and Remote Control, 55 (2), 263–270.
  44. Simulation Method Aids Pigging Operation in Subsea Waxy Crude Oil Pipelines – A Case Study. Available at: https://scienceon.kisti.re.kr/srch/selectPORSrchArticle.do?cn=NART77213912
  45. Gruzin, A. V., Tokarev, V. V., Shalai, V. V., Logunova, Y. V. (2015). The Artificial Additives Effect to Soil Deformation Characteristics of Oil and Oil Products Storage Tanks Foundation. Procedia Engineering, 113, 158–168. doi: https://doi.org/10.1016/j.proeng.2015.07.311
  46. Leonenkov, A. (2005). Nechetkoe modelirovanie v srede MATLAB i fuzzyTECH. Sankt-Peterburg: BHV, 736.
  47. Buhvalova, V. V. (2017). Paket prikladnyh programm FinPlus dlya resheniya zadach matematicheskogo programmirovaniya. Sankt-Peterburg: BHV, 327.
  48. Tazabekov, M. N., Muhambetkaliev, K. I. (2015). Modelirovanie, optimal'noe planirovanie i upravlenie magistral'nymi nefteprovodami. Almaty: Evero, 118.

Downloads

Published

2021-12-29

How to Cite

Orazbayev, B., Moldasheva, Z., Orazbayeva, K., Makhatova, V., Kurmangaziyeva, L., & Gabdulova, A. (2021). Development of mathematical models and optimization of operation modes of the oil heating station of main oil pipelines under conditions of fuzzy initial information. Eastern-European Journal of Enterprise Technologies, 6(2 (114), 147–162. https://doi.org/10.15587/1729-4061.2021.244949

Issue

Vol. 6 No. 2 (114) (2021): Information technology. Industry control systems

Section

Industry control systems

License

Copyright (c) 2021 Batyr Orazbayev, Zhadra Moldasheva, Kulman Orazbayeva, Valentina Makhatova, Lyailya Kurmangaziyeva, Aigul Gabdulova

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.