Hybrid Particle Swarm Optimization and Ant Colony Optimization Technique for the Optimal Design of Shell and Tube Heat Exchangers

Abstract Owing to the wide utilization of shell and tube heat exchangers (STHEs) in industrial processes, their cost minimization is an important target for both designers and users. Traditional design approaches are based on iterative procedures which gradually change the design and geometric parameters until satisfying a given heat duty and set of geometric and operational constraints. Although well proven, this kind of approach is time-consuming and may not lead to cost-effective design. The present study explores the use of non-traditional optimization technique called hybrid particle swarm optimization (PSO) and ant colony optimization (ACO), for design optimization of STHEs from economic point of view. The PSO applies for global optimization and ant colony approach is employed to update positions of particles to attain rapidly the feasible solution space. ACO works as a local search, wherein ants apply pheromone-guided mechanism to update the positions found by the particles in the earlier stage. The optimization procedure involves the selection of the major geometric parameters such as tube diameters, tube length, baffle spacing, number of tube passes, tube layout, type of head, baffle cut, etc. and minimization of total annual cost is considered as design target. The methodology takes into account the geometric and operational constraints typically recommended by design codes. Three different case studies are presented to demonstrate the effectiveness and accuracy of proposed algorithm. The examples analyzed show that the hybrid PSO and ACO algorithm provides a valuable tool for optimal design of heat exchanger. The hybrid PSO and ACO approach is able to reduce the total cost of heat exchanger as compare to cost obtained by previously reported genetic algorithm (GA) approach. The result comparisons with particle swarm optimizer and other optimization algorithms (GA) demonstrate the effectiveness of the presented method.

[1]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[2]  Reşat Selbaş,et al.  A new design approach for shell-and-tube heat exchangers using genetic algorithms from economic point of view , 2006 .

[3]  R. V. Rao,et al.  Design optimization of shell-and-tube heat exchanger using particle swarm optimization technique , 2010 .

[4]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[5]  M. Fesanghary,et al.  Design optimization of shell and tube heat exchangers using global sensitivity analysis and harmony search algorithm , 2009 .

[6]  Ali Kaveh,et al.  A HYBRID PARTICLE SWARM AND ANT COLONY OPTIMIZATION FOR DESIGN OF TRUSS STRUCTURES , 2008 .

[7]  A. M. Ranjbar,et al.  A hybrid of particle swarm and ant colony optimization algorithms for reactive power market simulation , 2006, J. Intell. Fuzzy Syst..

[8]  Urmila M. Diwekar,et al.  Optimal Design of Heat Exchangers: A Genetic Algorithm Framework , 1999 .

[9]  F. T. Mizutani,et al.  Mathematical Programming Model for Heat-Exchanger Network Synthesis Including Detailed Heat-Exchanger Designs. 1. Shell-and-Tube Heat-Exchanger Design , 2003 .

[10]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[11]  Eduardo M. Queiroz,et al.  Design optimization of shell-and-tube heat exchangers , 2008 .

[12]  F. T. Mizutani,et al.  Mathematical Programming Model for Heat-Exchanger Network Synthesis Including Detailed Heat-Exchanger Designs. 2. Network Synthesis , 2003 .

[13]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.

[14]  Urmila M. Diwekar,et al.  An automated approach for the optimal design of heat exchangers , 1997 .

[15]  Antonio Casimiro Caputo,et al.  Heat exchanger design based on economic optimisation , 2008 .

[16]  Donald Quentin Kern,et al.  Process heat transfer , 1950 .

[17]  Arturo Jiménez-Gutiérrez,et al.  Use of genetic algorithms for the optimal design of shell-and-tube heat exchangers , 2009 .

[18]  Sandip Kumar Lahiri,et al.  Particle swarm optimization technique for the optimal design of shell and tube heat exchangers , 2012 .

[19]  Patrick Siarry,et al.  Particle swarm and ant colony algorithms hybridized for improved continuous optimization , 2007, Appl. Math. Comput..