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Received July 11, 2011
Accepted January 16, 2012
articles This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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A hybrid optimization strategy for simultaneous synthesis of heat exchanger network

School of Energy and Power Engineering, Dalian University of Technology, Dalian 116023, China 1School of Mathematical Sciences, Dalian University of Technology, Dalian 116023, China
Korean Journal of Chemical Engineering, October 2012, 29(10), 1298-1309(12), 10.1007/s11814-012-0007-2
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Abstract

The heat exchanger network synthesis problem often leads to large-scale non-convex mixed integer nonlinear programming formulations that contain many discrete and continuous variables, as well as nonlinear objective function or nonlinear constraints. In this paper, a novel method consisting of genetic algorithm and particle swarm optimization algorithm is proposed for simultaneous synthesis problem of heat exchanger networks. The simultaneous synthesis_x000D_ problem is solved in the following two levels: in the upper level, the network structures are generated randomly and reproduced using genetic algorithm; and in the lower level, heat load of units and stream-split heat flows are optimized through particle swarm optimization algorithm. The proposed approach is tested on four benchmark problems, and the obtained solutions are compared with those published in previous literature. The results of this study prove that the presented method is effective in obtaining the approximate optimal network with minimum total annual cost as performance index.

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