Design and economic optimization of shell and tube heat exchangers using Artificial Bee Colony (ABC) algorithm

Abstract In this study, a new shell and tube heat exchanger optimization design approach is developed. Artificial Bee Colony (ABC) has been applied to minimize the total cost of the equipment including capital investment and the sum of discounted annual energy expenditures related to pumping of shell and tube heat exchanger by varying various design variables such as tube length, tube outer diameter, pitch size, baffle spacing, etc. Finally, the results are compared to those obtained by literature approaches. The obtained results indicate that Artificial Bee Colony (ABC) algorithm can be successfully applied for optimal design of shell and tube heat exchangers.

[1]  Nurhan Karaboga,et al.  A new design method based on artificial bee colony algorithm for digital IIR filters , 2009, J. Frankl. Inst..

[2]  K.M. Passino,et al.  Honey Bee Social Foraging Algorithms for Resource Allocation, Part I: Algorithm and Theory , 2007, 2007 American Control Conference.

[3]  Mingtian Xu,et al.  The application of field synergy number in shell-and-tube heat exchanger optimization design , 2009 .

[4]  B. V. Babu,et al.  Differential evolution strategies for optimal design of shell-and-tube heat exchangers , 2007 .

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

[6]  Frank Kreith,et al.  CRC Handbook of Thermal Engineering , 1999 .

[7]  Nicanor Quijano,et al.  Honey bee social foraging algorithms for resource allocation: Theory and application , 2010, Eng. Appl. Artif. Intell..

[8]  D.T. Pham,et al.  Optimising Neural Networks for Identification of Wood Defects Using the Bees Algorithm , 2006, 2006 4th IEEE International Conference on Industrial Informatics.

[9]  Junjie Li,et al.  Structural inverse analysis by hybrid simplex artificial bee colony algorithms , 2009 .

[10]  Igor Bulatov,et al.  Cost estimation and energy price forecasts for economic evaluation of retrofit projects , 2003 .

[11]  P. J. Pawar,et al.  Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms , 2010, Appl. Soft Comput..

[12]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[13]  Yavuz Özçelik,et al.  Exergetic optimization of shell and tube heat exchangers using a genetic based algorithm , 2007 .

[14]  Frank P. Incropera,et al.  Fundamentals of Heat and Mass Transfer , 1981 .

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

[16]  Kuppan Thulukkanam Heat Exchanger Design Handbook , 2013 .

[17]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

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

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

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