Economic optimization of a shell-and-tube heat exchanger (STHE) based on new method by Grasshopper Optimization Algorithm (GOA)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه مکانیک، واحد تاکستان، دانشگاه آزاد اسلامی، تاکستان، ایران

2 استادیار و عضو هیات علمی دانشگاه آزاد اسلامی واحد تاکستان

3 استادیار گروه محیط زیست، پژوهشکده انرژی و محیط زیست، پژوهشگاه نیرو، تهران، ایران

4 استادیار گروه مکانیک، واحد تاکستان، دانشگاه آزاد اسلامی، تاکستان، ایران

چکیده

Today, minimizing the cost of heat exchangers (HEs) is a major goal for the designer. In this study, a fast and reliable method is used to simulate, optimize design parameters and evaluate heat transfer enhancement and the economic optimization of STHE. Taking into account the importance of STHEs in industrial applications and the complexity in their geometry, the GOA methodology is adopted to obtain an optimal geometric configuration. The GOA is a metaheuristics search algorithm based on the mimics and simulates grasshoppers 'behavior in nature and the grasshoppers ' move to food sources. The total annual cost (TAC) (including the capital investment cost and the total operating cost consumption to overcome the pressure drop) is chosen as the objective function, and the design variables include number of tubes, number of tube passes, length of tubes, the arrangement of tubes, size and percentage of baffle cut, tube diameter, tubular step ratio have been considered. The developed algorithm is applied to two case studies and the results are compared with the original design and other optimization methods available in literature such as Genetic Algorithm (GA), Hybrid Genetic-Particle Swarm Optimization (GA-PSO), Gravitational Search Algorithm (GSA), Falcon Optimization Algorithm (FOA), Artificial Bee Colony (ABC), Bio- geography Based Optimization (BBO), Cuckoo Search Algorithm (CSA) and Firefly Algorithm (FFA). In order to investigate the feasibility of the proposed method, two case studies have been presented that show a significant TAC reduction of up to 30% with respect to traditional designed STHEs.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Economic optimization of a shell-and-tube heat exchanger (STHE) based on new method by Grasshopper Optimization Algorithm (GOA)

نویسندگان [English]

  • Amin Farzin 1
  • Mehrangiz Ghazi 2
  • Amir Farhang Sotoodeh 3
  • Mohammad Nikian 4
1 Department of Mechanical Engineering, Takestan Branch, Islamic Azad University, Takestan, Iran
3 Environment Group, Energy and Environment Faculty, Niroo Research Institute, tehran, Iran
4 Department of Mechanical Engineering, Takestan Branch, Islamic Azad University, Takestan, Iran
چکیده [English]

Today, minimizing the cost of heat exchangers (HEs) is a major goal for the designer. In this study, a fast and reliable method is used to simulate, optimize design parameters and evaluate heat transfer enhancement and the economic optimization of STHE. Taking into account the importance of STHEs in industrial applications and the complexity in their geometry, the GOA methodology is adopted to obtain an optimal geometric configuration. The GOA is a metaheuristics search algorithm based on the mimics and simulates grasshoppers 'behavior in nature and the grasshoppers ' move to food sources. The total annual cost (TAC) (including the capital investment cost and the total operating cost consumption to overcome the pressure drop) is chosen as the objective function, and the design variables include number of tubes, number of tube passes, length of tubes, the arrangement of tubes, size and percentage of baffle cut, tube diameter, tubular step ratio have been considered. The developed algorithm is applied to two case studies and the results are compared with the original design and other optimization methods available in literature such as Genetic Algorithm (GA), Hybrid Genetic-Particle Swarm Optimization (GA-PSO), Gravitational Search Algorithm (GSA), Falcon Optimization Algorithm (FOA), Artificial Bee Colony (ABC), Bio- geography Based Optimization (BBO), Cuckoo Search Algorithm (CSA) and Firefly Algorithm (FFA). In order to investigate the feasibility of the proposed method, two case studies have been presented that show a significant TAC reduction of up to 30% with respect to traditional designed STHEs.

کلیدواژه‌ها [English]

  • Economic Optimization
  • Shell-and-tube heat exchanger
  • Total Annual Cost
  • Grasshopper Optimization Algorithm
[1]         Smith, R. (2016). Chemical process design and integration. Second edition. Chichester, West Sussex, United Kingdom : John Wiley & Sons.
[2]         Chaudhuri, P. D., Diwekar, U. M., & Logsdon, J. S. (1997). An automated approach for the optimal design of heat exchangers. Industrial & engineering chemistry research36(9), 3685-3693.
[3]         Selbaş, R., Kızılkan, Ö., & 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: Process Intensification45(4), 268-275.
[4]         Caputo, A. C., Pelagagge, P. M., & Salini, P. (2008). Heat exchanger design based on economic optimisation. Applied thermal engineering28(10), 1151-1159.
[5]       Fesanghary, M., Damangir, E., & Soleimani, I. (2009). Design optimization of shell and tube heat exchangers using global sensitivity analysis and harmony search algorithm. Applied Thermal Engineering29(5-6), 1026-1031.
[6]         Patel, V. K., & Rao, R. V. (2010). Design optimization of shell-and-tube heat exchanger using particle swarm optimization technique. Applied Thermal Engineering30(11-12), 1417-1425.
[7]         Şahin, A. Ş., Kılıç, B., & Kılıç, U. (2011). Design and economic optimization of shell and tube heat exchangers using Artificial Bee Colony (ABC) algorithm. Energy Conversion and Management52(11), 3356-3362.
[8]         Hadidi, A., & Nazari, A. (2013). Design and economic optimization of shell-and-tube heat exchangers using biogeography-based (BBO) algorithm. Applied Thermal Engineering51(1-2), 1263-1272.
[9]         Hajabdollahi, H., Ahmadi, P., & Dincer, I. (2011). Thermoeconomic optimization of a shell and tube condenser using both genetic algorithm and particle swarm. International journal of refrigeration34(4), 1066-1076.
[10]      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 Research43(8), 3711-3722.
[11]      Rao, R. V., & Patel, V. (2013). Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Applied Mathematical Modelling37(3), 1147-1162.
[12]      Karimi H., Ahmadi‐Danesh‐Ashtiani H, Aghanajafi C.(2019). Optimization of the total annual cost in a shell and tube heat exchanger by Ant colony optimization technique. Iranian Journal of Marine Technologies, 6(3), 128-136.
[13]      Rao, R. V., & Saroj, A. (2018). Multi-objective design optimization of heat exchangers using elitist-Jaya algorithm. Energy Systems9(2), 305-341.
[14]      Özçelik, Y. (2007). Exergetic optimization of shell and tube heat exchangers using a genetic based algorithm. Applied Thermal Engineering27(11-12), 1849-1856.
[15]      Şahin, A. Ş., Kılıç, B., & Kılıç, U. (2011). Design and economic optimization of shell and tube heat exchangers using Artificial Bee Colony (ABC) algorithm. Energy Conversion and Management52(11), 3356-3362.
[16]      Hadidi, A., Hadidi, M., & Nazari, A. (2013). A new design approach for shell-and-tube heat exchangers using imperialist competitive algorithm (ICA) from economic point of view. Energy Conversion and Management67, 66-74.
[17]      Asadi, M., Song, Y., Sunden, B., & Xie, G. (2014). Economic optimization design of shell-and-tube heat exchangers by a cuckoo-search-algorithm. Applied thermal engineering73(1), 1032-1040.
[18]      Turgut, O. E., Turgut, M. S., & Coban, M. T. (2014). Design and economic investigation of shell and tube heat exchangers using Improved Intelligent Tuned Harmony Search algorithm. Ain Shams Engineering Journal5(4), 1215-1231.
[19]      Yang, J., Oh, S. R., & Liu, W. (2014). Optimization of shell-and-tube heat exchangers using a general design approach motivated by constructal theory. International Journal of heat and mass transfer77, 1144-1154.
[20]      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 Conversion and Management93, 84-91.
[21]      Azad, A. V., & Azad, N. V. (2016). Application of nanofluids for the optimal design of shell and tube heat exchangers using genetic algorithm. Case Studies in Thermal Engineering8, 198-206.
[22]      Caputo, A. C., Pelagagge, P. M., & Salini, P. (2016). Manufacturing cost model for heat exchangers optimization. Applied Thermal Engineering94, 513-533.
[23]      Rao, R. V., & Saroj, A. (2017). Economic optimization of shell-and-tube heat exchanger using Jaya algorithm with maintenance consideration. Applied Thermal Engineering116, 473-487.
[24]      Rao, R. V., & Saroj, A. (2017). Constrained economic optimization of shell-and-tube heat exchangers using elitist-Jaya algorithm. Energy128, 785-800.
[25]      Venkata Rao, R., & Saroj, A. (2018). Constrained Economic Optimization of Shell-and-Tube Heat Exchangers Using a Self-Adaptive Multipopulation Elitist-Jaya Algorithm. Journal of Thermal Science and Engineering Applications10(4).
[26]      Iyer, V. H., Mahesh, S., Malpani, R., Sapre, M., & Kulkarni, A. J. (2019). Adaptive Range Genetic Algorithm: A hybrid optimization approach and its application in the design and economic optimization of Shell-and-Tube Heat Exchanger. Engineering Applications of Artificial Intelligence85, 444-461.
[27]      Roy, U., & Majumder, M. (2019). Economic optimization and energy analysis in shell and tube heat exchanger by meta-heuristic approach. Vacuum166, 413-418.
[28]      Rao, R. V., Saroj, A., Ocloń, P., & Taler, J. (2019). Design Optimization of Heat Exchangers with Advanced Optimization Techniques: A Review. Archives of Computational Methods in Engineering, 1-32.
[29]      Sai, J. P., & Rao, B. N. (2020). Efficiency and economic optimization of shell and tube heat exchanger using bacteria foraging algorithm. SN Applied Sciences2(1), 13.
[30]      Sanaye S, Hajabdollahi H. (2010). Multi-objective optimization of shell and tube heat exchangers. Applied Thermal Engineering30:1937–1945.
[31]      Wong, J. Y., Sharma, S., & Rangaiah, G. P. (2016). Design of shell-and-tube heat exchangers for multiple objectives using elitist non-dominated sorting genetic algorithm with termination criteria. Applied Thermal Engineering93, 888-899.
[32]      Mirzaei, M., Hajabdollahi, H., & Fadakar, H. (2017). Multi-objective optimization of shell-and-tube heat exchanger by constructal theory. Applied Thermal Engineering125, 9-19.
[33]      Fettaka, S., Thibault, J., & Gupta, Y. (2013). Design of shell-and-tube heat exchangers using multiobjective optimization. International Journal of Heat and Mass Transfer60, 343-354.
[34]      Yang, J., Fan, A., Liu, W., & Jacobi, A. M. (2014). Optimization of shell-and-tube heat exchangers conforming to TEMA standards with designs motivated by constructal theory. Energy conversion and management78, 468-476.
[35]      de Vasconcelos Segundo, E. H., Amoroso, A. L., Mariani, V. C., & dos Santos Coelho, L. (2017). Economic optimization design for shell-and-tube heat exchangers by a Tsallis differential evolution. Applied Thermal Engineering111, 143-151.
[36]      Daróczy, L., Janiga, G., & Thévenin, D. (2014). Systematic analysis of the heat exchanger arrangement problem using multi-objective genetic optimization. Energy65, 364-373.
[37]      Amini, M., & Bazargan, M. (2014). Two objective optimization in shell-and-tube heat exchangers using genetic algorithm. Applied thermal engineering69(1-2), 278-285.
[38]      Mohanty, D. K. (2016). Application of firefly algorithm for design optimization of a shell and tube heat exchanger from economic point of view. International Journal of Thermal Sciences102, 228-238.
[39]      Daróczy, L., Janiga, G., & Thévenin, D. (2014). Systematic analysis of the heat exchanger arrangement problem using multi-objective genetic optimization. Energy65, 364-373.
[40]      Guo, J., & Xu, M. (2012). The application of entransy dissipation theory in optimization design of heat exchanger. Applied Thermal Engineering36, 227-235.
[41]      Guo, J., Huai, X., Li, X., Cai, J., & Wang, Y. (2013). Multi-objective optimization of heat exchanger based on entransy dissipation theory in an irreversible Brayton cycle system. Energy63, 95-102.
[42]      Caputo, A. C., Pelagagge, P. M., & Salini, P. (2015). Heat exchanger optimized design compared with installed industrial solutions. Applied Thermal Engineering87, 371-380.
[43]      Roy, U., Majumder, M., & Barman, R. N. (2017). Designing configuration of shell-and-tube heat exchangers using grey wolf optimisation technique. International Journal of Automation and Control11(3), 274-289.
[44]      Khosravi, R., Khosravi, A., Nahavandi, S., & Hajabdollahi, H. (2015). Effectiveness of evolutionary algorithms for optimization of heat exchangers. Energy conversion and management89, 281-288.
[45]      Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: theory and application. Advances in Engineering Software105, 30-47.
[46]      Kraus, A. D., Welty, J. R., & Aziz, A. (2011). Introduction to Thermal and Fluid Engineering. CRC Press.
[47]      Serth, R. W., & Lestina, T. (2014). Process heat transfer: Principles, applications and rules of thumb. Academic Press.
[48]      Shah, R. K., & Sekulic, D. P. (2003). Fundamentals of heat exchanger design. John Wiley & Sons.
[49]      Bhatti, M. S. (1987). Turbulent and transition flow convective heat transfer in ducts. Handbook of single-phase convective heat transfer.
[50]      Ayub, Z. H. (2005). A new chart method for evaluating single-phase shell side heat transfer coefficient in a single segmental shell and tube heat exchanger. Applied Thermal Engineering25(14-15), 2412-2420.
[51]      Sinnott, R. K., Coulson, J. M., & Richardson, J. F. (2005). Chemical Engineering Design, vol. 6. Printed at Butterworth Heinemann, An Imprint of Elsevier, Linacre house, Jordan Hill, Oxford OX2 8DP30, 450-457.
[52]      Ullah, I., Khitab, Z., Khan, M. N., & Hussain, S. (2019). An efficient energy management in office using bio-inspired energy optimization algorithms. Processes7(3), 142.
[53]      Taal, M., Bulatov, I., Klemeš, J., & Stehlı́k, P. (2003). Cost estimation and energy price forecasts for economic evaluation of retrofit projects. Applied thermal engineering23(14), 1819-1835.
[54]      de Vasconcelos Segundo, E. H., Mariani, V. C., & dos Santos Coelho, L. (2019). Design of heat exchangers using Falcon Optimization Algorithm. Applied Thermal Engineering156, 119-144.
[55]      Karimi, H., Ashtiani, H. A. D., & Aghanajafi, C. (2019). Study of mixed materials heat exchanger using optimization techniques. Journal of Engineering, Design and Technology.
[56]      Mohanty, D. K. (2016). Gravitational search algorithm for economic optimization design of a shell and tube heat exchanger. Applied Thermal Engineering107, 184-193.
[57]      Kern, D. Q. (1997). Process heat transfer. Tata McGraw-Hill Education.