Optimization of the total annual cost in mixed materials heat exchanger network by detailed equipment design using particle swarm technique

Yıl 2020, Cilt: 23 Sayı: 3, 216 - 222, 27.08.2020

Habib Karimi Hossein Danesh Ashtiani Cyrus Aghanajafi

https://doi.org/10.5541/ijot.710239

Cited By: 1

Öz

This paper investigated an optimization model for heat exchanger network by detailed design method for each heat exchanger unit. Particle swarm optimization technique is used to determine to optimum results in the mixed material heat exchanger network which included the capital and energy costs of heat exchangers. Mixed materials used in corrosive flows and included three types of exchangers such as cheap, expensive and mixed. Two methods used for achieving the optimum the total annual cost in mixed materials heat exchanger networks such as total and partial decompositions methods. The case study was used to show the application of the proposed method. The total annual cost has been reduced by detailed design in each heat exchanger by optimization technique. It has been reduced 20.7% compared with initial case in full integration method. The reduction of the total annual cost by detailed design in the partial decomposition method is 12.4% compared with the full integration method and 5.78% compared with the total decomposition method.

Anahtar Kelimeler

: heat exchanger network, , mixed material, detailed design, Particle swarm optimization

Kaynakça

• [1].Marzbanrad, J., Sharifzadegan, A. and Kahrobaeian, A. (2007) “Thermodynamic Optimization of GSHPS Heat Exchangers” International Journal of Thermodynamics, Volume 10, Issue 3, pp. 107 – 112.
• [2]. Gundersen, T., Naess, L. (1998) “The synthesis of cost optimal heat exchanger networks-an industrial review of the state of the art” Computers and Chemical Engineering, 12(6), pp. 503–530.
• [3]. Furman, K., Sahinidis, N. (2002) “Critical Review and Annotated Bibliography for Heat Exchanger Network Synthesis in the 20th Century” Industrial & Engineering Chemistry Research, 41(10), pp. 2335-2370.
• [4]. Grossmann, I., Caballero, J., Yeomans, H. (2000)“Advances in mathematical programming for the synthesis of process systems” Latin American Applied Research, 30(4), pp. 263–284.
• [5]. Bjork, K. and Westerlund, T. (2002) “Global optimization of heat exchanger network synthesis problems with and without the isothermal mixing assumption” Computers and Chemical Engineering, 26, pp.1581–1593.
• [6]. Karimi, H., Danesh Ashtiani, H.A. and Aghanajafi, C. (2019) "Study of mixed materials heat exchanger using optimization techniques", Journal of Engineering, Design and Technology,17(2), pp.414-433.
• [7]. Hall, S.G. and Ahmad, S. (1990) “Capital cost targets for heat exchanger networks comprising mixed materials of construction, pressure ratings and exchanger types”, Computers Chemical Engineering, Vol. 14 No. 3, pp. 319-335.
• [8]. Karimi H., Ahmadi‐Danesh‐Ashtiani H, Aghanajafi C. (2019) “Applying multiple decomposition methods and optimization techniques for achieving optimal cost in mixed materials heat exchanger networks. International Journal of Energy Research, 43, pp. 3711–3722.
• [9]. Patel, V. and Rao, R. (2010) “Design optimization of shell-and-tube heat exchanger using particle swarm optimization technique” Applied Thermal Engineering, 30(12), pp. 1417-1425.
• [10]. Selbas, R., Kızılkan, O. and Reppich, M. (2006) “A new design approach for shell-and-tube heat exchangers using genetic algorithms from economic point of view” Chemical Engineering Process, Vol. 45, pp. 268-275.
• [11]. Caputo, A., Pelagagge, P. and Salini, P. (2008) “Heat exchanger design based on economic optimization” Applied Thermal Engineering, 28(10), pp. 1151-1159.
• [12]. Hilbert, R., Janiga, G., Baron, R. and Thevenin, D. (2006)” “Multi-objective shape optimization of a heat exchanger using parallel genetic algorithms”, International Journal of Heat and Mass Transfer, 49(16), pp. 2567-2577.
• [13]. Ozkol, I. and Komurgoz, G. (2005) “Determination of the optimum geometry of the heat exchanger body via a genetic algorithm”, International Journal of Heat and Mass Transfer, 48, pp. 283-296.
• [14]. Ravagnani, M. and Caballero, J. (2007) “Optimal heat exchanger network synthesis with the detailed heat transfer equipment design” Computers and Chemical Engineering, 31, pp.1432–1448.
• [15]. Ponco-Otega, J., Serna-González, M. and Jiménez-Gutiérrez, A. (2008) “Synthesis of multi pass heat exchanger networks using genetic algorithms” Computers & Chemical Engineering, (32), 10, pp. 2320-2332.
• [16]. Rajagopal, T., Surana, A., Koppula, J., Harshit, S. and Kumar, U. (2017) "Numerical investigation of shell and tube heat exchanger using Al2O3 nanofluid". International Journal of Thermodynamics, 20, pp.59-68
• [17]. Kennedy, J. and Eberhart, R. (1995) “Particle swarm optimization” Proceedings of the IEEE International Conference on Neural Networks Perth, Australia, pp.1942-1948.
• [18]. Patel, V. and Rao, R. (2010) “Design optimization of shell-and-tube heat exchanger using particle swarm optimization technique” Applied Thermal Engineering, 30(12), pp. 1417-1425.
• [19]. Taal, M., Bulatov, I., Klemes, J. and Stehlik, P. (2003) “Cost estimation and energy price forecast for economic evaluation of retrofit projects” Applied Thermal Engineering, Vol. 23, No. 14, pp. 1819-1835.