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

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Hala A. Abdel-Halim, Othman E. A., A. A. Sakr, A. A. Zaki, A. A. Abouelsoud

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

	author = {Hala A. Abdel-Halim and Othman E. A. and A. A. Sakr and A. A. Zaki and 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 = {13},
	url = {},
	doi = {10.5120/ijca2017914113},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


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Particle Swarm Optimization; Genetic Algorithm; Forced Circulation Evaporator; Performance Indices.