Synthesis of multipass heat exchanger networks using genetic algorithms |
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| Authors: | José María Ponce-Ortega Medardo Serna-González Arturo Jiménez-Gutiérrez |
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| Affiliation: | 1. Departamento de Ingeniería Química, Instituto Tecnológico de Celaya, Celaya, Gto. Mexico;2. Facultad de Ingeniería Química, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Mich. Mexico;1. Department of Chemical Engineering, University of Cape Town, Private Bag, Rondebosch 7701, South Africa;2. Faculty of Chemistry and Chemical Engineering, University of Maribor, Slovenia;1. Department of Chemical and Environmental Engineering/Centre of Sustainable Palm Oil Research (CESPOR), The University of Nottingham, Malaysia Campus, Broga Road, 43500 Semenyih, Selangor, Malaysia;2. Crops for the Future Research Centre, The University of Nottingham, Malaysia Campus, Broga Road, 43500 Semenyih, Selangor, Malaysia;3. Department of Chemical and Biological Engineering, University of Wisconsin – Madison, WI 53706, USA;1. Sustainable Process Integration Laboratory – SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology – VUT BRNO, Technická 2896/2, 616 69 Brno, Czech Republic;2. Department of Chemical Engineering, University of Cape Town, Private Bag X3, 7701 Rondebosch, South Africa;3. Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia |
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| Abstract: | In this work, a methodology based on genetic algorithms (GAs) is developed for the optimal synthesis of multipass heat exchanger networks (HENs). The network model is based on a stagewise superstructure, and the problem of finding the optimum number of 1–2 shells in series of multipass heat exchangers is aided by an efficient optimization model that uses the standard FT design method. The proposed methodology allows for proper handling of the trade-offs involving energy consumption, number of units, number of 1–2 shells and network area to provide a network with the minimum total annual cost. The results of the examples show that the new approach is able to find more economical networks than those generated by other methods. |
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