Design optimization of shell and tube heat exchangers using global sensitivity analysis and harmony search algorithm |
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| Authors: | M Fesanghary E Damangir I Soleimani |
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| Affiliation: | 1. Department of Mechanical Engineering, Amirkabir University of Technology, 424-Hafez Avenue, 15875-4413 Tehran, Iran;2. Department of Chemical Engineering, Amirkabir University of Technology, 424-Hafez Avenue, 15875-4413 Tehran, Iran;1. School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;2. Mechanical Science and Engineering, University of Illinois, Urbana, IL, USA;1. Pontifical Catholic University of Paraná (PUCPR), Mechanical Engineering Graduate Program (PPGEM), Curitiba, PR, Brazil;2. Pontifical Catholic University of Paraná (PUCPR), Industrial and Systems Engineering Graduate Program (PPGEPS), Curitiba, PR, Brazil;3. Federal University of Technology of Paraná (UTFPR), Academic Department of Electronics, Curitiba, PR, Brazil;4. Federal University of Paraná (UFPR), Department of Electrical Engineering, Curitiba, PR, Brazil;1. Mechanical Engineering Department, Faculty of Engineering, University of Isfahan, Isfahan, Iran;2. Mechanical Engineering Department, School of Energy, Kermanshah University of Technology, Kermanshah, Iran;1. School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China;2. School of Chemical Engineering and Technology, Xi’an Jiaotong University, Xi’an 710049, China |
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| Abstract: | This study explores the use of global sensitivity analysis (GSA) and harmony search algorithm (HSA) for design optimization of shell and tube heat exchangers (STHXs) from the economic viewpoint. To reduce the size of the optimization problem, non-influential geometrical parameters which have the least effect on total cost of STHXs are identified using GSA. The HSA which is a meta-heuristic based algorithm is then applied to optimize the influential geometrical parameters. To demonstrate the effectiveness and accuracy of the proposed algorithm, an illustrative example is studied. Comparing the HSA results with those obtained using genetic algorithm (GA) reveals that the HSA can converge to optimum solution with higher accuracy. |
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