Neural network controller identification for refining process | Научно-инновационный портал СФУ

Neural network controller identification for refining process

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Scientific Conference on Applied Physics, Information Technologies and Engineering (APITECH) / 2-nd International Scientific and Practical Conference on Borisov's Readings; Siberian Fed Univ, Polytechn Inst, Krasnoyarsk, RUSSIA; Siberian Fed Univ, Polytechn Inst, Krasnoyarsk, RUSSIA

Год издания: 2019

Идентификатор DOI: 10.1088/1742-6596/1399/4/044095

Аннотация: The article discusses the task of identifying a neural network controller for the installation of rectification of oil refining production. A rectification process research model is used to evaluate the effectiveness of the controller. The control parameters of the rectification process that are used to identify the controller and evaluate its effectiveness are determined. In a numerical study, the possibility of using a neural network controller to control the rectification process is shown. As a basic option for a comparative study, we used a PID-regulator, which is the standard version in production today. The advantage of a neural network controller in controlling processes in the context of the implementation of various target trajectories is shown. The proposed model of a neural network controller can be adapted and used for computer control of the rectification process. © Published under licence by IOP Publishing Ltd.

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Издание

Журнал: Journal of Physics: Conference Series

Выпуск журнала: Vol. 1399, Is. 4

Номера страниц: 44095

ISSN журнала: 17426588

Издатель: Institute of Physics Publishing

Авторы

  • Bukhtoyarov V.V. (Siberian Fed Univ, 79 Svobodny Pr, Krasnoyarsk 660041, Russia; Reshetnev Siberian State Univ Sci & Technol, 31 Krasnoyarsky Rabochy Av, Krasnoyarsk 660037, Russia)
  • Tynchenko V.S. (Siberian Fed Univ, 79 Svobodny Pr, Krasnoyarsk 660041, Russia; Reshetnev Siberian State Univ Sci & Technol, 31 Krasnoyarsky Rabochy Av, Krasnoyarsk 660037, Russia)
  • Petrovskiy E.A. (Siberian Fed Univ, 79 Svobodny Pr, Krasnoyarsk 660041, Russia)
  • Bashmur K.A. (Siberian Fed Univ, 79 Svobodny Pr, Krasnoyarsk 660041, Russia)
  • Kukartsev V.V. (Siberian Fed Univ, 79 Svobodny Pr, Krasnoyarsk 660041, Russia; Reshetnev Siberian State Univ Sci & Technol, 31 Krasnoyarsky Rabochy Av, Krasnoyarsk 660037, Russia)
  • Bukhtoyarova N.A. (Siberian Fed Univ, 79 Svobodny Pr, Krasnoyarsk 660041, Russia)

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