Pareto Optimum Design of Heat Exchangers based on the Imperialist Competitive Algorithm: A Case Study

Subject Areas : optimization and simulation

Mohammadjavad  Mahmoodabadi ¹ (Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran)
Soodeh  Zarnegar ² (Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran)

Received: 2018-04-08 Accepted : 2018-08-25 Published : 2018-09-01

Keywords: Tube Heat Exchangers, Imperialist Competitive Algorithm, Multi-Objective Optimization, Shell,

Abstract :

In this paper, the multi-objective optimum design of shell and tube heat exchangers is investigated. A thermal modelling of an industrial shell and tube heat exchanger is performed using an -NTU method for estimating the shell side heat transfer coefficient and pressure drop. The efficiency and total cost (includes the capital investment for the equipment and operating cost) are two important parameters in the design of heat exchangers. The fixed parameters and the ranges of the design variables are obtained from a shell and tube recovery heat exchanger in Barez tire production factory located in Kerman city, Iran. The Imperialist Competitive Algorithm (ICA) is used to find the optimal design parameters to achieve the maximum thermal efficiency and minimum consumption cost as the objective functions. The tube inside and outside diameters, tube length and the number of tubes are considered as four design variables. Furthermore, the effects of changing the values ​​of the design variable on the objective functions are independently investigated. At the end, the obtained Pareto front and the related design variables and their corresponding objective functions are presented.

References:

[1]     Costa, L. H., Queiroz, M., Design Optimization of Shell-and-Tube Heat Exchangers, Applied Thermal Engineering, Vol. 28, 2008, pp. 1798-1805.

[2]     Ramananda Rao, K., Shrinivasa, U., and Srinivasan, J., Synthesis of Cost Optimal Shell and Tube Heat Exchangers, Heat Transfer Engineering, Vol. 12, No. 3, 1991, pp. 47-55.

[3]     Ponce-Ortega, J. M., Serna-Gonzalez, M., Salcedo-Estrada, L. I., and Jimenez-Gutierrez, A., Minimum-Investment Design of Multiple Shell and Tube Heat Exchangers Using a MINLP Formulation, Chemical Engineering Research and Design, Vol. 84, No. 10, 2006, pp. 905-910.

[4]     Ponce-Ortega, J. M., Serna-Gonzalez, M., and Jimenez-Gutierrez, A., Use of Genetic Algorithms for the Optimal Design of Shell-and-Tube Heat Exchangers, Applied Thermal Engineering, Vol. 29, 2009, pp. 203-209.

[5]     Agarwal, A., Gupta, S. K., Jumping Gene Adaptations of NSGA-II and their Use in the Multi-Objective Optimal Design of Shell and Tube Heat Exchangers, Chemical Engineering Research and Design, Vol. 86, 2008, pp. 123-139.

[6]     Sanaye, S., Hajabdollahi, H., Multi-Objective Optimization of Shell and Tube Heat Exchangers”, Applied Thermal Engineering, Vol. 30, 2010, pp. 1937-1945.

[7]     Taborek, J., Industrial Heat Exchanger Design Practices, Wiley, New York, 1991.

[8]     Kakac, S., Liu, H., Heat Exchangers Selection Rating, and Thermal Design, CRC Press, New York, 2000.

[9]     Shah, R. K., Sekulic, P., Fundamental of Heat Exchanger Design, John Wiley & Sons, 2003.

[10]  Taal, M., Bulatov, I., Klemes, J., and Stehlik, P., Cost Estimation and Energy Price Forecasts for Economic Evaluation of Retrofit Projects, Applied Thermal Engineering, Vol. 23, 2003, pp. 1819-1835.

[11]  Caputo, A. C., Pelagagge, P. M., and Salini, P., Heat Exchanger Design based on Economic Optimization, Applied Thermal Engineering, Vol. 28, 2008, pp. 1151-1159.

[12]  Atashpaz-Gargari, E., Lucas, C., Imperialist Competitive Algorithm: an Algorithm for Optimization Inspired by Imperialistic Competition, IEEE Congress on Evolutionary Computation, 2007, pp. 4661–4666.

[13]  Nazari-Shirkouhi, S., Eivazy, H., Ghodsi, R., Rezaie, K., and Atashpaz-Gargari, E., Solving the Integrated Product Mix-Outsourcing Problem by a Novel Meta-Heuristic Algorithm: Imperialist Competitive Algorithm, Expert Systems with Applications, Vol. 37, No. 12, 2010, pp. 7615–7626.

[14]  Mahmoodabadi, M. J., Taherkhorsandi, M., and Talebipour, M., Adaptive Robust PID Sliding Control of a Liquid Level System based on Multi-Objective Genetic Algorithm Optimization, Journal of Control and Cybernetics, Vol. 46, No. 3, 2017, pp. 227-246. 

[15]  Mahmoodabadi, M. J., Nemati, A. R., A Novel Adaptive Genetic Algorithm for Global Optimization of Mathematical Test Functions and Real-world Problems, Engineering Science and Technology, an International Journal, Vol. 19, No. 4, 2016, pp. 2002-2021.

[16]  Mahmoodabadi, M. J., Taherkhorsandi, M., Optimal Robust Design of Sliding-mode Control based on Multi-Objective Particle Swarm Optimization for Chaotic Uncertain Problems, International Journal of Advanced Design and Manufacturing Technology, Vol. 10, No. 3, 2017, pp. 115-126. 

[17]  Farokhi, A., Mahmoodabadi, M. J., Optimal Fuzzy Inverse Dynamics Control of a Parallelogram Mechanism based on a New Multi-Objective PSO, Cogent Engineering, Vol. 5, No. 1, 2018, pp. 1-20.

[18]  Atashpaz-Gargari, E., Rajabioun, R., Hashemzadeh, F., and Salmasi, F. R., A Decentralized PID Controller based on Optimal Shrinkage of Gershgorin Bands and PID Tuning Using Colonial Competitive Algorithm, International Journal of Innovative Computing, Information and Control, Vol. 5, 2009, pp. 3227–3240. 

[19]  Sepehri Rad, H., Lucas, C., Application of Imperialistic Competition Algorithm in Recommender Systems, In Proceedings of the 13th Int'l CSI Computer Conference, Kish Island, Iran, 2008. 

[20]  Jasour, A., Atashpaz-Gargari, E., and Lucas, C., Vehicle Fuzzy Controller Design Using Imperialist Competitive Algorithm, Second Iranian Joint Congress on Fuzzy and Intelligent Systems, Mashhad, Iran, 2008. 

[21]  Khabbazi, A., Atashpaz-Gargari, E., and Lucas, C., Imperialist Competitive Algorithm for Minimum Bit Error Rate Beam Forming, International Journal of Bio-Inspired Computation, Vol. 1, 2009, pp. 125–133. 

[22]  Alikhani Koupaei, J., Abdechiri, M., An Optimization Problem for Evaluation of Image Segmentation Methods, International Journal of Computer and Network Security, Vol. 2, No. 6, 2010, pp. 142-149. 

[23]  Sayadnavard, M. H., Haghighat, A. T., and Abdechiri, M., Wireless Sensor Network Localization Using Imperialist Competitive Algorithm, 3rd IEEE International Conference on Computer Science and Information Technology, 2010.

[24]  Jolai, F., Sangari, M., and Babaie, M., Pareto Simulated Annealing and Colonial Competitive Algorithm to Solve an Offline Scheduling Problem with Rejection, Journal of Engineering Manufacture, Vol. 224, No. 7, 2010, pp. 1119–1131. 

[25]  Shokrollahpour, E., Zandieh, M., and Dorri, B., A Novel Imperialist Competitive Algorithm for Bi-Criteria Scheduling of the Assembly Flowshop Problem, International Journal of Production Research, Vol. 49, No. 11, 2011, pp. 3087-3103. 

[26]  Forouharfard, S., Zandieh, M., An Imperialist Competitive Algorithm to Schedule of Receiving and Shipping Trucks in Cross-Docking Systems, International Journal of Advanced Manufacturing Technology, Vol. 51, No. 9, 2010, pp. 1179-1193. 

[27]  Karimi, N., Zandieh, M., and Najafi, A. A., Group Scheduling in Flexible Flow Shops: A Hybridised Approach of Imperialist Competitive Algorithm and Electromagnetic-Like Mechanism, International Journal of Production Research, Vol. 49, No. 16, 2011, 4965-4977. 

[28]  Bagher, M., Zandieh, M., and Farsijani, H., “Balancing of Stochastic U-Type Assembly Lines: an Imperialist Competitive Algorithm, International Journal of Advanced Manufacturing Technology, Vol. 54, No. 1, 2010, pp. 271-285. 

[29]  Sarayloo, F., Tavakkoli-Moghaddam, R., Imperialistic Competitive Algorithm for Solving a Dynamic Cell Formation Problem with Production Planning, Advanced Intelligent Computing Theories and Applications, Lecture Notes in Computer Science, Vol. 6215, 2010, pp. 266–276.

[30]  Piroozfard, H., Wong, K. Y., An Imperialist Competitive Algorithm for the Job Shop Scheduling Problems, IEEE International Conference on Industrial Engineering and Engineering Management, (IEEM), 2014, pp. 69–73.

[31]  Biabangard-Oskouyi, A., Atashpaz-Gargari, E., Soltani, N., and Lucas, C., Application of Imperialist Competitive Algorithm for Material Properties Characterization from Sharp Indentation Test, International Journal of Engineering Simulation, Vol. 10, No. 1, 2009, pp. 1-8. 

[32]  Yousefi, M., Mohammadi, H., Second Law Based Optimization of a Plate Fin Heat Exchanger Using Imperialist Competitive Algorithm, International Journal of the Physical Sciences, Vol. 6, No. 20, 2011, pp. 4749–4759.

[33]  Mousavi Rad, S. J., Akhlaghian Tab, F., and Mollazade, K., Application of Imperialist Competition Algorithm for Feature Selection: a Case Study on Rice Classification, International Journal of Computer Application, Vol. 40, No. 16, 2012, pp. 41-48.

[34]  Lucas, C., Nasiri-Gheidari, Z., and Tootoonchian, F., Application of an Imperialist Competitive Algorithm to the Design of a Linear Induction Motor, Energy Conversion and Management, Vol. 51, No. 7, 2010, pp. 1407–1411.

[35]  Movahhedi, O., Salmasi, F. R., Optimal Design of Propulsion System with Adaptive Fuzzy Controller for a PHEV based on Non-Dominated Sorting Genetic and Colonial Competitive Algorithms International Review of Automatic Control, Vol. 2, No. 4, 2009, pp. 445–451.

[36]  Niknam, T., Taherian Fard, E., Pourjafarian, N., and Rousta, A., An Efficient Hybrid Algorithm based on Modified Imperialist Competitive Algorithm and K-Means for Data Clustering, Engineering Applications of Artificial Intelligence, Vol. 24, No. 2, 2011 pp. 306–317.

[37]  Mozafari, H., Abdi, B., and Ayob, A., Optimization of Transmission Conditions for Thin Interphase Layer Based on Imperialist Competitive Algorithm, International Journal on Computer Science and Engineering, Vol. 2, No. 7, 2010, pp. 2486–2490.

[38]  Arora, J. S., Introduction to Optimum Design, Fourth Edition, Academic Press.

[39]  Mahmoodabadi, M. J., Taherkhorsandi, M., and Bagheri, A., Optimal Robust Sliding Mode Tracking Control of a Biped Robot based on Ingenious Multi-objective PSO, Neurocomputing, Vol. 124, 2014, pp. 194–209.

[40]  Bisheban, M., Mahmoodabadi, M. J., Pareto Optimal Design of Decoupled Sliding Mode Control based on a New Multi-Objective Particle Swarm Optimization Algorithm, Amirkabir International Journal of Science & Research (Modeling, Identification, Simulation & Control), Vol. 45, No. 2, 2013, pp. 31- 40.