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Cost Optimization of Cooling Tower Geometry using Hybrid Optimization Technique

Published on March 2012 by K. C. More, R. D. Rakhade
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International Conference in Computational Intelligence
Foundation of Computer Science USA
ICCIA - Number 6
March 2012
Authors: K. C. More, R. D. Rakhade
961aaf30-09b7-4558-9a1f-5f483bc6311c

K. C. More, R. D. Rakhade . Cost Optimization of Cooling Tower Geometry using Hybrid Optimization Technique. International Conference in Computational Intelligence. ICCIA, 6 (March 2012), 26-30.

@article{
author = { K. C. More, R. D. Rakhade },
title = { Cost Optimization of Cooling Tower Geometry using Hybrid Optimization Technique },
journal = { International Conference in Computational Intelligence },
issue_date = { March 2012 },
volume = { ICCIA },
number = { 6 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 26-30 },
numpages = 5,
url = { /proceedings/iccia/number6/5133-1045/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Computational Intelligence
%A K. C. More
%A R. D. Rakhade
%T Cost Optimization of Cooling Tower Geometry using Hybrid Optimization Technique
%J International Conference in Computational Intelligence
%@ 0975-8887
%V ICCIA
%N 6
%P 26-30
%D 2012
%I International Journal of Computer Applications
Abstract

We use Hybrid GSA algorithm is developed by combining the good characteristic of two existing non-traditional optimization algorithms namely genetic algorithm (GA) and simulated annealing (SA). GA and SA algorithms are both independently valid approaches with certain strengths and weaknesses. Out of this GA is best for finding global optimum solution and SA is good for finding local optimum solution. This Hybrid GSA algorithm used for design optimization of natural draft wet cooling tower. The hybrid GSA problem is formulated so as to Minimizing the total annual cost and that consists of fixed charges plus operating costs. The results obtained using proposed algorithm is compared with those obtained by using GA and SA individually as well as Leap-frog Optimization Program with Constraints (LFOPC) optimization algorithm model result.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Wet-cooling tower Geometry optimization hybrid optimization Genetic and Simulated annealing algorithm