•  
  •  
 

Abstract

Increasing energy efficiency and reducing fuel consumption in the process of generating electricity and heat at thermal power plants is one of the urgent tasks. Such systems operate under conditions of random changes in external and internal influences, as well as measurement uncertainties, which reduce the quality of control. In order to overcome this problem, it was proposed to develop an intelligent control system using the quantum photon-spin method to control technological units of thermal power plants. In the proposed approach, a multi-dimensional heating boiler device was taken as a control object, and the simulation modeling of the control system was solved using the quantum photon spin method. As a result, accurate and fast control of technological process parameters, reduction of energy resources and fuel consumption are achieved.

First Page

56

Last Page

61

References

  1. Cirac, J. I., & Zoller, P. (2010). Goals and opportunities in quantum simulation. Nature Physics, 6, 264–266.
  2. Ulyanov, S. V., Mishin, A. A., & Minogin, A. A. (2010). Informatsionnaya tekhnologiya proektirovaniya robastnykh baz znaniy nechetkikh regulyatorov. Ch. III: kvantovyy nechetkij vyvod i kvantovaya informatsiya [Information technology for designing robust knowledge bases of fuzzy controllers. Part III: Quantum fuzzy inference and quantum information]. Sistemnyy analiz v nauke i obrazovanii, (3), 423–430. Dubna.
  3. Usmanov, K. I., Sarbolaev, F. N., Islomova, F. K., & Yakubova, N. S. (2020). Adaptivno nechetkoye sinergeticheskoye upravleniye mnogomernykh nelineynykh dinamicheskikh obyektov [Adaptive fuzzy synergistic control of multidimensional nonlinear dynamic systems]. Universum: Tekhnicheskiye nauki, (3-1(72)), 24–28.
  4. Lee, J., Shung, W., Kim, E., & Kim, S. (2010). A new genetic approach structure learning of Bayesian networks: matrix genetic algorithm. International Journal of Control, Automation and Systems, 8(4), 398–407.
  5. Avedyan, E. D., Galushkin, A. I., & Pantyukhin, D. V. (2011). Assotsiativnaya neyronnaya set SMAS i yeye modifikatsii v zadache raspoznavaniya obrazov [Associative neural network SMAS and its modifications in the pattern recognition problem]. Informatsionnyye tekhnologii. Novyye tekhnologii, (7), 63–71.
  6. El-Madany, H. T., Fahmy, F. H., El-Rahman, N. M. A., & Dorrah, H. T. (2011). Artificial intelligence techniques for controlling spacecraft power system. Proceedings of the International Conference on Renewable Energies and Power Quality, Las Palmas de Gran Canaria, Spain, 163–172.
  7. Ulyanov, S. V., & Nefedov, N. Y. (2012). Effektivnost i kachestvo intellektualnogo upravleniya s primeneniem kvantovogo nechetkogo vyvoda: globalno neustoychivaya dinamicheskaya sistema [Efficiency and quality of intelligent control using quantum fuzzy inference: Globally unstable dynamic system]. Sistemnyy analiz v nauke i obrazovanii, (1). Retrieved from http://www.sanse.ru/archive/23
  8. Ablayev, F., & Ablayev, M. (2015). On the concept of cryptographic quantum hashing. Laser Physics Letters, 12(12), 125204. https://doi.org/10.1088/1612-2011/12/12/125204
  9. Rastegar, S., Araújo, R., Sadati, J., & Mendes, J. (2017). A novel robust control scheme for LTV systems using output integral discrete-time synergetic control theory. European Journal of Control, 34. https://doi.org/10.1016/j.ejcon.2016.12.006
  10. Eshbobaev, J., Norkobilov, A., Usmanov, K., Khamidov, B., Kodirov, O., & Avezov, T. (2024). Control of wastewater treatment processes using a fuzzy logic approach. Engineering Proceedings, 67(1), 39.
  11. Usmanov, K. I., Yakubova, N. S., Urmanova, V. T., & Abdurasulova, G. E. (2023). Synthesis of a control system for the process of diesel fuel hydropurifying with the Adar method. E3S Web of Conferences, 458, 01025. EDP Sciences.
  12. Sidikov, I. K., Usmanov, K. I., Yakubova, N. S., & Kazakhbaev, S. A. (2020). Nechetkoe sinergeticheskoe upravlenie nelineynykh sistem [Fuzzy synergetic control of nonlinear systems]. Journal of Advances in Engineering Technology, (2), 16–19. https://doi.org/10.24412/2181-1431-2020-2-16-19
  13. Yakubova, N. S., & Abdurasulova, G. E. (2023). Issledovanie nechetkikh regulyatorov v sistemakh intellektualnogo upravleniya na osnove kvantovykh vychisleniy [Study of fuzzy regulators in intelligent control systems based on quantum computing]. Universum: Tekhnicheskiye nauki, (3-1(108)), 22–25.
  14. Usmanov, K., Eshbobaev, J., & Yakubova, N. (2023). Modeling and optimization of the ammonium solution extraction process. Engineering Proceedings, 56(1), 198.
  15. Yakubova, N. (2025). Application of quantum algorithms in optimization of cognitive decision-making systems. Chemical Technology, Control and Management, 2(8). https://doi.org/10.59048/2181-1105.1658
  16. Isar, A., Sandulescu, A., Scutaru, H., Stefanescu, E., & Scheid, W. (1994). Open quantum systems. International Journal of Modern Physics E, 3(2), 635–714.
  17. Gebhart, V., Santagati, R., Gentile, A. A., Gauger, E. M., Craig, D., Ares, N., ... & Bonato, C. (2023). Learning quantum systems. Nature Reviews Physics, 5(3), 141–156.
  18. Rivas, A., & Huelga, S. F. (2012). Open quantum systems (Vol. 10, pp. 978–3). Berlin: Springer.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.