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An Intelligent Control System Design for an Evaporator based on Particle Swarm Optimization

by Hala A. Abdel-Halim, Othman E. A., A. A. Sakr, A. A. Zaki, A. A. Abouelsoud
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 166 - Number 9
Year of Publication: 2017
Authors: Hala A. Abdel-Halim, Othman E. A., A. A. Sakr, A. A. Zaki, A. A. Abouelsoud
10.5120/ijca2017914113

Hala A. Abdel-Halim, Othman E. A., A. A. Sakr, A. A. Zaki, A. A. Abouelsoud . An Intelligent Control System Design for an Evaporator based on Particle Swarm Optimization. International Journal of Computer Applications. 166, 9 ( May 2017), 17-29. DOI=10.5120/ijca2017914113

@article{ 10.5120/ijca2017914113,
author = { Hala A. Abdel-Halim, Othman E. A., A. A. Sakr, A. A. Zaki, A. A. Abouelsoud },
title = { An Intelligent Control System Design for an Evaporator based on Particle Swarm Optimization },
journal = { International Journal of Computer Applications },
issue_date = { May 2017 },
volume = { 166 },
number = { 9 },
month = { May },
year = { 2017 },
issn = { 0975-8887 },
pages = { 17-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume166/number9/27698-2017914113/ },
doi = { 10.5120/ijca2017914113 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:13:15.760562+05:30
%A Hala A. Abdel-Halim
%A Othman E. A.
%A A. A. Sakr
%A A. A. Zaki
%A A. A. Abouelsoud
%T An Intelligent Control System Design for an Evaporator based on Particle Swarm Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 166
%N 9
%P 17-29
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The main contribution of this paper is aimed to design and implementation of an intelligent level controller and intelligent 2×2 decentralized PI controller and a lead compensator for the forced circulation evaporator by using PSO strategy. The most important thing to guarantee the safe operation of the forced circulation evaporator, without damaging the installed equipment, is obtaining optimal controllers for the evaporator operating pressure and the level of liquid inside the separator part. Also the percent of the concentration of the non-volatile in the solution must be effectively controlled to required limits. PSO algorithm is implemented in MATLAB and is compared to GA strategy for design and implementation of optimal controllers for the evaporator system by minimizing the summation of the characteristics of unit step response. Also computer simulation results are compared to the different two cost functions methods by analyzing the performance, stability and robustness with respect to variation of the evaporator control system.

References
  1. N. Gowtham; Shobha Shankar, “PI tuning of Shunt Active Filter using GA and PSO algorithm”, 2nd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB),(2016) 207 – 213, IEEE Conference Publications
  2. V. Pano; P. R. Ouyang, “Comparative study of GA, PSO, and DE for tuning position domain PID controller”,IEEE International Conference on Robotics and Biomimetics, (ROBIO 2014) 1254 – 1259, IEEE Conference Publications
  3. Sheikh Abid Hossain; Sourav Roy; Animesh Karmaker; Md. Rafiqul Islam , “Performance improvement of PID controller for AVR system using Particle Swarm Optimization”, 2015 International Conference on Advances in Electrical Engineering (ICAEE), (2015) 243 – 246, IEEE Conference Publications
  4. Guangyu Li; Chen Guo; Yanxin Li; Wu Deng, “Fractional-Order PID Controller of USV Course-Keeping Using Hybrid GA-PSO Algorithm”, 2015 8th International Symposium on Computational Intelligence and Design (ISCID)( 2015), Vol. 2, pp. 506 - 509, IEEE Conference Publications
  5. El-Sayed M. Ahmed; Mohamed. M. Ismail, “PID controller tunning scheme for TRMS using AI techniques”, 2012 8th International Computer Engineering Conference (ICENCO), (2012) 37 - 42, IEEE Conference Publications
  6. K. Jagatheesan; B. Anand; Nillanjan Dey; Tarek Gaber; Aboul Ella Hassanien; Tai-Hoon Kim, “A Design of PI Controller using Stochastic Particle Swarm Optimization in Load Frequency Control of Thermal Power Systems”, 2015 Fourth International Conference on Information Science and Industrial Applications (ISI), (2015) 25 – 32, IEEE Conference Publications
  7. Fariborz Mirlou Miavagh; Easa Ali Abbasi Miavaghi; Amir Rikhtegar Ghiasi;Mostafa Asadollahi, "Applying of PID, FPID, TID and ITID controllers on AVR system using particle swarm optimization (PSO)”,2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), IEEE Conference Publications
  8. Hengameh Noshahri; Hamed Kharrati, “PID controller design for unmanned aerial vehicle using genetic algorithm”, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), (2014) 213 – 217, IEEE Conference Publications
  9. Naeim Farouk Mohammed; Xiuzhen Ma; Enzhe Song, “Tuning of PID controller for diesel engines using genetic algorithm”, 2013 IEEE International Conference on Mechatronics and Automation, (2013) 1523 – 1527, IEEE Conference Publications
  10. Naeim Farouk Mohammed; Enzhe Song; Xiuzhen Ma; Qaisar Hayat, “Tuning of PID controller of synchronous generators using genetic algorithm”, 2014 IEEE International Conference on Mechatronics and Automation, (2014) 1544 – 1548, IEEE Conference Publications
  11. Singh, R.; Kuchhal, P.; Choudhury, S.; and Gehlot, A., ‘‘Implementation and Evaluation of Heating System using PID with Genetic Algorithm’’, Indian Journal of Science and Technology, Vol.8 No. 5(2015) 413–8.
  12. Deepa Thangavelusamy, Lakshmi Ponnusamy, “Comparison of PI controller tuning using GA and PSO for a Multivariable Experimental Four Tank System”, International Journal of Engineering and Technology (IJET) , Vol.5. No. 6, (Dec 2013-Jan 2014) 4660-4671.
  13. Angeline Vijula Dhanraj and Devarajan Nanjundappan ,“Design of Optimized PI Controller with ideal Decoupler for A non Linear Multivariable System Using Particle Swarm Optimization”, International Journal of Innovative Computing, Information and Control ICIC International, 2014 ISSN 1349-4198, Vol. 10. No. 1, (February 2014 ) 341-355
  14. Boubertakh, H.; Labiod, S.; and M., ‘‘PSO to Design Decentralized Fuzzy PI Controllers Application for a Helicopter’’, 20th Mediterranean Conference on Control & Automation (MED), Barcelona, Spain, 2012.
  15. Zhao, S.-Z., Iruthayarajan, M. Willjuice; Baskar, S.; Suganthan, P.N., ‘‘objective robust PID controller tuning using two lbests multi objective particle swarm optimization’’, Information Sciences, Vol. 181 No. 16(2011) 3323-3335.
  16. Iruthayarajan, M. Willjuice and Baskar, S. ‘‘Evolutionary algorithms based design of multivariable PID controller’’, Expert Systems with Applications, Vol. 36 No. 5(2009) 9159-9167.
  17. Han, Kai; Zhao, Jun; Xu, Zu-hua; and Qian, Ji-xin. ‘‘A closed-loop particle swarm optimizer for multivariable process controller design’’, Journal of Zhejiang University-Science A, Vol. 9 No. 8( 2008) 1050-1060.
  18. Jiangjiang Wang, Youyin Jing and Chunfa Zhang, “Genetic optimization algorithm on PID decoupling controller for variable flow heating system”, Industrial Electronics and Applications, 2008. ICIEA 2008. 3rd IEEE Conference on, 3-5 June 2008.
  19. Su, C. T.; Wong, J. T., ‘‘Designing MIMO controller by neuro-traveling particle swarm optimizer approach’’, International Expert Systems with Applications, 32(2007) 848-855, available:http://homes.esat.kuleuven.be/~smc/daisy/daisydata.html
  20. Chang, W. D., ‘‘A multi-crossover genetic approach to multivariable PID controllers tuning’’, International Expert Systems with Applications, 33(2007) 620–626.
  21. Haupt, R. L. and Haupt, S. E., ‘‘Practical Genetic Algorithms’’, 2nd ed., John Wiley &Sons, Inc., 2004.
  22. Kennedy, J. and Eberhart R. , ‘‘Particle swarm optimization’’, in Proceedings of IEEE International Conference of Neural Networks, Vol. IV, 1995 Perth, Australia, pp. 1942-1948.
  23. Zamani, M., Karimi-Ghartemani, M., Sadati, N. and Parniani, M., “Design of a fractional order PID controller for an AVR using particle swarm ptimization”, Control Eng Pract 2009;17(12):1380–7.
  24. Hamidi, J., “Control System Design Using Particle Swarm Optimization (PSO)”, and International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, Vol. 1, Issue-6, January 2012, pp. 116-119.
  25. Abdel-Halim, Hala A. et al., “ A Comparative Study of Intelligent Control System Tuning Methods for an Evaporator Based on Genetic Algorithm”, International Journal of Applied Information Systems (IJAIS), ISSN : 2249-0868, Foundation of Computer Science FCS, New York, USA, Volume 11– No.10,Febrauary 2017 – available on : www.ijais.org
  26. Stanley M. Shinners,” Modern Control System Theory and Design”, 1998,  John Wiley and Sons Ltd, New York, United States.
  27. Hassanzadeh, Iraj and Mobayen, Saleh, ‘‘Optimum design of PID controller for 5-bar-linkage manipulator using particle swarm optimization’’, in Proceeding of the 4th International Symposium on Mechatronics and its applications.
  28. Clerc, M., ‘‘The Swarm and the queen: towards a deterministic and adaptive particle swarm optimization’’, in Proceedings of the Conference on Evolutionary Computation, (1999). 1951–7. Applications (ISMA07), Sharjah, U.A.E., 2007.
  29. MATLAB Version 8.3.0.532 R2014a, 32-bit (win32), February 11, 2014.
  30. Newell, R. B. and Lee, P. L., ‘‘Applied Process Control: A case study’’, Prentice-Hall of Australia Ltd., 1989.
Index Terms

Computer Science
Information Sciences

Keywords

Particle Swarm Optimization Genetic Algorithm Forced Circulation Evaporator Performance Indices.

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