•  
  •  
 

Abstract

An intelligent method of controlling a rectification column, which implements the technological process of separation of multicomponent mixtures by rectification method, is proposed. The advantage of the method is that it uses an advanced process control system (APC-system) and a distributed control system at the same time. The proposed intellectual method of controlling the process of rectification of multicomponent mixtures for the application of APC-systems and a net model of situational control allows to increase the quality of the product coming from the rectification column, and the efficiency of the rectification of the mixture column, to increase the quality of the control of the process of rectification and to obtain energy savings. In the research work, a functional scheme of automation was developed, which serves to implement the control system of the rectification process of multicomponent mixtures. The APC-system, acting as an advanced control system, calculates the values of relevance functions using a virtual analyzer, a fuzzy model of situational control. The fuzzy model of situational control has the ability to calculate the possible situations and transfer them to the decision-maker using the relevance function expressed through linguistic variables. The programmable logic controller and I/O module included in the distributed control system provide timely delivery of measurement information and coordination of control decisions. The decision-making block in the control system allows to evaluate the quality indicators of the product on the basis of fuzzy logic. The concentration of the extracted components was selected as a quality indicator of the finished product. Factors affecting concentration are taken as terms, and a model for predicting uncertain situations is obtained. To calculate ambiguous situations, it is proposed to use the software called “Formation of ambiguous standard situations using linguistic variables in the control of the rectification process”. In order to check the effectiveness of the proposed intelligent control method, regulation contours with simple PID-controller and fuzzy PIDcontroller were created using the Matlab application programming package. Descriptions of the transition process were obtained by studying regulation contours. The results show that the fuzzy PID-controller tuning loop has better control quality indicators. The proposed intelligent method of control the process of rectification of mixtures allows to improve the quality of the product and increase the quality of control.

First Page

148

Last Page

154

References

  1. N.R. Yusupbekov., R.A. Aliev., R.R. Aliev., A.N. Yusupbekov. Intelligent control and decision-making systems. Textbook. -Tashkent “National encyclopedia of Uzbekistan”, 2014. 490.
  2. G.I. Madrusenko. “Principles of creation and development of automated process control systems: models: Moscow”, 2000. 449.
  3. S.V. Artemova. Methodology for constructing intelligent information and control systems for heat-technological devices: “Tambov: TSTU”, 2014. 425.
  4. A.Y. Torgashov. Control of optimal static modes of distillation columns based on non-linear process models: diss... cand. tech. science. “Vladivostok Vlad GUES”, 2000. 144.
  5. P.A. Aliev., A.E. Tserkovny., G.A. Mamedova. Production management with fuzzy initial information. Textbook. –Moscow “Energy nuclear publisher”. 1991. 240.
  6. G.M. Ostrovsky., Y.M. Volin. New problems and theory of flexibility and optimization of chemical-technological processes with uncertainty. “Journal theoretical foundations of chemical technology”, 1999. 578-590.
  7. M.J. Mohideem., J.D. Perkins., E.F. Pistikopoulos. Optimal design of dynamic systems under uncertainty. “Journal AIChE”, 1996. 2251-2272.
  8. Patent for the method (RU) No. patent 2509593S1, “Published on 20.03” 2014, Bulletin No. 8.
  9. Patent for the method (RU) No. patent 2558596S2, “Published on 10.08”, 2015, Bulletin No. 22.
  10. Patent for the method (RU) No. patent 2724772S1, “Published on 25.06”, 2020, Bulletin No. 18.
  11. Y.Sh. Avazov. Governance Model of the stochastic process of rectification of multicomponent mixtures based on fuzzy logic. Journal advances in intelligent systems and computing. “Switzerland: Springer Nature”, 2021. 364.
  12. Y.Sh. Avazov., U.T. Usmanov. Formation of fuzzy reference situations using linguistic variables in the control of rectification processes. Intellectual Property Agency of the Republic of Uzbekistan. “Certificate No. DGU 04970”, 2018.

Download

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.