MULTI-OBJECTIVE THERMAL DESING OPTIMIZATION OF A SHELL AND TUBE CONDENSER THROUGH GLOBAL BEST ALGORITHM

Year 2017, Volume: 19 Issue: 56, 644 - 665, 01.05.2017

Oğuz Emrah Turgut

Abstract

This study considers Global Best Algorithm (GBEST) for thermoeconomic design of a shell and tube condenser. Design process sustained by the traditional procedures involves tedious and exhaustive iterative calculations which sometimes becomes time consuming and may not lead to economically optimum configuration. Literature studies have shown that solution strategy offered by stochastic optimization methods such as Global Best Algorithm over thermal design of any kind of heat exchanger is promising solution strategy according to the optimum results found in each study. Firstly, optimization performance of the GBEST is assessed with ten benchmark problems and numerical outcomes are compared with those obtained from different literature optimization methods. A case study taken from literature has been solved by GBEST along with famous optimizers of Particle Swarm Optimization and Differential Evolution in the framework of single and multi objective optimization so as to optimize the problem objectives of total cost of heat exchanger and average overall heat transfer coefficient. GBEST not only finds more favourable results than those obtained from the compared optimization algorithms, but also improves the preliminary design taken from literature study. Pareto curve is constructed for multi objective optimization and best solution on the curve is selected by three renowned decision making methods of LINMAP, TOPSIS, and Shannon’s entropy theory. Finally, a sensitivity analysis has been performed in order to observe the variational influences of design parameters over optimization objectives

Keywords

Global Best Algorithm, Multi objective optimization, Shell and tube heat exchanger, Thermal design

GÖVDE BORU TİPLİ KONDENSERLERİN GLOBAL ENİYİ ARAMA ALGORİTMASIYLA ÇOK AMAÇLI TERMAL TASARIM OPTİMİZASYONU

Abstract

Bu çalışmada Global Eniyi Arama algoritması gövde borulu
düzenli bir kondenserin termal tasarımını oluşturmak için
kullanılmıştır. Konvensiyonel optimizasyon algoritmaları tarafından sağlanan tasarım süreci, zaman alıcı olmasının yanısıra
ekonomik açıdan da beklenen sonuçları sağlayamayabilmektedir.
Literatür çalışmaları Global Eniyi Arama algoritması gibi
stokastik optimizasyon algoritmalarının herhangi bir ısı
değiştiricinin termal tasarımında uygulanmasının literatürde
yapılan diğer çalışmalardan elde edilen sonuçlara dayanarak
oldukça olumlu çıktılar verdiğini göstermektedir. Bu çalışmada ilk
olarak, Global Eniyi Arama algoritmasının optimizasyon
performansı 10 adet optimizasyon test fonksiyonu kullanılarak
değerlendirilmiştir. Literatür çalışmalarından alınan bir örnek
optimizasyon problemi Global Eniyi Arama algoritması ile birlikte
Diferansiyel Evrim ve Parçacık Sürü Optimizasyon algoritmaları
tarafından minimum toplam ısı değiştirici maliyeti ve maksimum
toplam ısı transferi katsayısı gibi amaç fonksiyonlarını optimize
etmek için tek ve çok amaçlı optimizasyon yöntemleri
kullanılarak çözülmüştür. Global Eniyi Arama algoritması diğer
karşılaştırılan algoritmalardan daha olumlu sonuçlar elde etmekle
kalmamış ayrıca örnek optimizasyon probleminde tasarlanan
değerlerin gelişmesinde önemli bir rol oynamıştır. Çok amaçlı
optimizasyon için birbirine üstünlük kuramayan sonuçlardan
oluşan Pareto eğrisi inşa edilmiş ve eğri üzerindeki en iyi sonuç
LINMAP, TOPSIS ve Shannon’un entropi teorisi gibi üç önemli
karar verme mekanizması tarafından seçilmiştir. Çalışmanın
sonunda ise hassasiyet analizi uygulanarak tasarım
parametrelerinin optimizasyon amaç fonskiyonları üzerindeki
değişimsel etkileri gözlemlenmiştir.

Keywords

Global Eniyi Arama algoritması, Çok amaçlı optimizasyon, Gövde borulu ısı değiştirgeci, Termal tasarım

References

• Asadi, M., Song, Y., Sunden, B, Xie, G. 2014. design of shell and tube heat exchangers by a cuckoo search algorithm, Engineering, Vol.73, pp.1032 – 1040. Thermal Kakaç, S., Liu, H.,
• Pramuanjaroenkij, A. 2012. Heat Exchangers Selection, Rating, and Thermal Design, Boca Raton: Taylor and Francis, 102p.
• Kern, D.Q. 1950.Process Heat Transfer, McGraw-Hill, p.305.
• Rosenhow, W.M., Hartnett, P.J. 1973. Handbook of Heat Transfer, McGraw-Hill, 467p.
• Shah, R.K., Bell, K.J. 2000. The CRC Handbook of Thermal Engineering, CRC Press, 372p.
• Fabbri, G. 2000. Heat transfer optimization in corrugated wall channels, International Journal of Heat and Mass Transfer, Vol. 43, pp.4299 – 4310.
• Bintoro, J.S., Akbarzadeh, A., Mochizuki, M. 2005. A closed-loop electronics implementing impinging jet and mini channels heat exchanger, Applied Thermal Engineering, Vol.25, pp.2740 – 2753. by phase
• Mariani, V.C., Duck, A.R.K., Guerra, F.A., Coelho, L.S., Rao, R.V. 2012. A chaotic quantum-behaved particle swarm approach applied to optimization of heat exchangers, Applied Vol.42, pp.119 – 128.
• Mohanty, D.K. 2016. Application
• of firefly algorithm for design optimization of a shell and tube heat exchanger from economic point of view, Intenational Journal of Thermal Sciences, Vol. 102, pp.228 – 238.
• Caputo, A.C., Pelagagge, P.M., Salini, P. 2008. Heat exchanger design based on economic optimization, Applied Vol.28, pp.1151 – 1159.
• Hadidi, A., Hadidi, M., Nazaifi, A. 2013. A new design approach for shell and tube heat exchangers using algorithm (ICA) from economic point of view, Energy Conversion and Management, Vol.67, pp.66-74.
• Hadidi, A., Nazari, A. 2013. Design and economic optimization of shell and tube heat exchangers using biogeography-based algorithm, Engineering, Vol.51, pp.1263-1272. Thermal Applied
• Khosravi, R., Khosravi, A., Nahavandi, S., Hajabdollahi H, 2015. Effectiveness of evolutionary algorithms for optimization of heat exchangers, Energy Conversion and Management, Vol. 89, pp.281-288.
• Selbaş, R., Kızılkan, O., Reppich, M. 2006. A new design approach for shell and tube heat exchangers using genetic algorithms from economic point of view, Chemical Engineering and Processing, Vol.45, pp.268 – 275.
• Şahin, A.S., Kılıc, B., Kılıc, U. 2011. Design and economic optimization of shell and tube heat exchangers using Artificial Bee Colony (ABC) algorithm, Energy Conversion and Management, Vol.52, pp.3356 – 3362.
• Sadeghzadeh, H., Ehyaei, M.A., Rosen, M.A. 2015. Techno- economic optimization of a shell and tube heat exchanger by genetic and particle swarm algorithms, Energy Management, Vol. 93, pp.84-91.
• Sanaye, S., Hajabdollahi, H. 2010. Multi objective optimization of shell and tube heat exchangers, Applied Vol.30, pp.1937 – 1945.
• Patel, V.K., Rao, R.V. 2010. Design optimization of shell and tube heat exchanger using particle swarm, Applied Vol.30, pp.1417 - 1425.
• Sun, S., Lu, Y., Yan, C. 1993. Optimization in calculation of shell heat International Communication in Heat and Mass Transfer,Vol.20, pp.675 – 685. exchanger,
• Ozcelik, Y. 2007. Exergetic optimization of shell and tube heat exchangers using genetic based algorithm, Engineering, Vol. 27, pp.1846 – 1856. Thermal
• Hajabdollahi, H., Ahmadi, P., Dıncer I. 2012. Exergetic optimization of shell and tube heat exchnagers using NSGA – II, Heat Transfer Engineering, Vol.33, pp.618 – 628.
• Haseli, Y., Dincer, I., Natarer, G.F. 2010. Exergy efficiency of two phase flow in a shell and tube condenser, Engineering, Vol.31, pp.17 – 24.
• Kovarik, M. 1989. Optimal heat exchnagers, Journal of Heat Transfer, Vol.111, pp.1846 – 1856.
• Fax, D.H., Mills, R.R. 1957. Generalized exchanger Transactions, Vol.79, pp.653 – 661. ASME
• Unuvar, A., Kargici, S. 2004. An approach for optimum design of heat exchanegrs, An approach for optimum exchangers, International Journal of Energy Research, Vol. 28, pp.1379
• Afimiwala, K.A. 1976. Interactive computer methods for design optimization, Mechanical Department, State University of New York at Buffola Thesis, Engineering
• Paul, H. 1982. An application of geometric programming to heat exchanger design, Computers and Industrial pp.103 – 114. Vol.6,
• Radhakrishnan, V.R., Gupta, B.R., Jairaman, V. 1980. Optimum design of shell and tube heat exchangers by geometric programming, Indian Journal of Technology, Vol.18, pp.293 – 300.
• Babu, B.V., Mohiddin, S.B. 1999. Automated exchangers intelligence based optimization, In: Proceedings of International Symposium and 52nd Annual Session of IIChE (CHEMCON - 99), Punjab University, Chandigarh, December 20 – 23.
• Tayal, M.C., Fu, Y., Diwekar, U.M. 1999. Optimal design of heat exchangers: a genetic algorithm framework, Industrial Engineering Chemical Research, Vol.38, pp.456 – 467.
• Babu, B.V., Munawar, S.A. 2007. Differential evolution strategies for optimal design of shell and tube heat Engineering Science, Vol.62, pp.3720 – 3739. Chemical
• Hajabdollahi, H., Ahmadi, P., Dincer, I. optimization of shell and tube condenser using both genetic and particle Journal of Refrigeration, Vol.34, pp.1066 – 1076.
• Chaudhuri, P.D., Urmila, M.D., Jefery, S.L. 1997. An automated approach for the optimal design of heat exchangers, Industrial & Engineering Chemistry Research, Vol.36, pp.3685 – 3693.
• Haseli, Y., Dincer, I., Natarer, G.F. 2008. Optimum temperatures in a shell and tube condenser with respect to exergy, International Journal of Heat and Mass Transfer, Vol.51, pp.2462 – 2470.
• Khalifeh Soltan, B., Saffar-Avval, M., Damangir, E. 2004. Minimizing capital and operating costs of shell condensers using optimum baffle spacing, Applied Thermal Engineering, Vol. 24, pp.2801 – 2810.
• Taal, M., Bulatov, I., Klemes, J., Stehlik, P. 2003. Cost estimation and energy price forecasts for economic evaluation of retrofit projects, Applied Thermal Energy, Vol.23, pp.1819-1835.
• Civicioglu, P. 2013. Backtracking Search Optimization Algorithm for numerical optimization problems, Applied Computation, Vol. 219, pp. 8121 – 8144. and
• Yadav, P., Kumar, R., Panda, S.K., Chang, C.S., 2012. An Intelligent Tuned Harmony Search algorithm for Sciences, Vol.196, pp.47-72. Information
• Yang, X.S. 2010. A New Metaheuristic Algorithm, In: Nature Inspired Cooperative Optimization (NISCO 2010)” Studies In Computatonal Intelligence, Vol. 284, pp.65 – 74. for
• Sun, J., Feng, B., Xu, W.B. 2004. Particle swarm optimization with particles having quantum behavior, IEEE Proceedings of Congress on Evolutionary Computation, pp.325
• Erol, O.K., Eksin, I. 2006. A new optimization method:Big Bang-Big Crunch, Advances in Engineering Software, Vol. 37, pp.106 – 111.
• Civicioglu, P., 2012. Transforming geocentric cartesian coordinates to geodetic corrdinates by using differential Computers & Geosciences, Vol.46, pp.229 – 247. algorithm,
• Kennedy, J., Eberhart, R. 1995. Swarm Particle In:Proceeedings International Conference on Neural Networks.IV, pp. 1942 – 1948.
• Storn, R., Price, K. 1997. Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, Vol. 11, pp. 341 – 359.
• Turgut, O.E., Coban, M.T. 2017. Thermal design of spiral heat exchangers and heat pipes through global best algorithm, Heat and Mass Transfer, Vol. 53, pp.899-916
• Arora, R., Kaushik, S.C., Kumar, R., Arora, R. 2016. Soft computing based multi-objective optimization of Brayton cycle power plant with isothermal heat addition using evolutionary decision making, Applied Soft Computing, Vol. 46, pp.267-283.