MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION OF THE K-TYPE SHELL AND TUBE HEAT EXCHANGER (CASE STUDY)

Year 2021, , 570 - 583, 01.03.2021

M. Nadi M. A. Ehyaei A. Ahmadi O. E. Turgut

https://doi.org/10.18186/thermal.888261

Cited By: 1

Abstract

This paper investigated optimization of two objectives function include the total amount of heat transfer between two mediums and the total cost of shell and tube heat exchanger. The study was carried out for k-type heat exchanger of the cryogenic unit of gas condensates by multiple objective particle swarm optimization. Six decision variables including pipe pitch ratio, pipe diameter, pipe number, pipe length, baffle cut ratio, and baffle distance ratio were taking into account to conduct this simulation-based research. The results of mathematical modeling confirmed the actual results (data collected from the evaporator unit of the Tehran refinery’s absorption chiller). The optimization results revealed that the two objective functions of heat transfer rate and the total cost were in contradiction with each other. The results of the sensitivity analysis showed that with change in the pitch ratio from 1.25 to 2, the amount of heat transfer was reduced from 420 to 390 kW about 7.8%. Moreover, these variations caused reduction in cost function from 24,500 to 23,500 $, less than 1%. On the other hand, an increase in pipe length from 3 to 12 meters, the heat transfer rate raised from 365 to 415 kW by 13.7%, while the cost increased from 20,000$ to 24500$ about 22%.

Keywords

Cryogenic, Heat exchanger, Optimization, Objective

References

• [1] Caputo AC, Pelagagge PM, Salini P. Manufacturing cost model for heat exchangers optimization. Applied Thermal Engineering. 2016;94:513-33.
• [2] Ehyaei MA. Estimation of condensate mass flow rate during purging time in heat recovery steam generator of combined cycle power plant. Thermal Science. 2014;18(4):1389-97.
• [3] Mozafari A, Ehyaei MA. Effects of Regeneration Heat Exchanger on Entropy, Electricity Cost, and Environmental Pollution Produced by Micro Gas Turbine System. International Journal of Green Energy. 2012;9(1):51-70.
• [4] Sadeghzadeh H, Aliehyaei M, Rosen MA. Optimization of a Finned Shell and Tube Heat Exchanger Using a Multi-Objective Optimization Genetic Algorithm. Sustainability. 2015;7(9):11679-95.
• [5] Sadeghzadeh H, Ehyaei M, Rosen M. Techno-economic optimization of a shell and tube heat exchanger by genetic and particle swarm algorithms. Energy Conversion and Management. 2015;93:84-91.
• [6] Shah RK, Sekulic DP. Fundamentals of heat exchanger design. 2003.
• [7] Yang J, Fan A, Liu W, Jacobi AM. Optimization of shell-and-tube heat exchangers conforming to TEMA standards with designs motivated by constructal theory. Energy Conversion and Management. 2014;78:468-76.
• [8] Yang J, Oh S-R, Liu W. Optimization of shell-and-tube heat exchangers using a general design approach motivated by constructal theory. International Journal of Heat and Mass Transfer. 2014;77:1144-54.
• [9] Aliehyaei M, Atabi F, Khorshidvand M, Rosen MA. Exergy, economic and environmental analysis for simple and combined heat and power IC engines. Sustainability. 2015;7(4):4411-24.
• [10] Asgari E, Ehyaei M. Exergy analysis and optimisation of a wind turbine using genetic and searching algorithms. International Journal of Exergy. 2015;16(3):293-314.
• [11] Ashari G, Ehyaei M, Mozafari A, Atabi F, Hajidavalloo E, Shalbaf S. Exergy, economic, and environmental analysis of a PEM fuel cell power system to meet electrical and thermal energy needs of residential buildings. Journal of Fuel Cell Science and Technology. 2012;9(5):051001.
• [12] Shamoushaki M, Ehyaei M, Ghanatir F. Exergy, economic and environmental analysis and multi-objective optimization of a SOFC-GT power plant. Energy. 2017;134:515-31.
• [13] Shamoushaki M, EHYAEI MA. EXERGY, ECONOMIC, AND ENVIRONMENTAL (3E) ANALYSIS OF A GAS TURBINE POWER PLANT AND OPTIMIZATION BY MOPSO ALGORITHM. Thermal Science. 2018;22(6A):2641-51.
• [14] Shamoushaki M, Ghanatir F, Ehyaei M, Ahmadi A. Exergy and exergoeconomic analysis and multi-objective optimisation of gas turbine power plant by evolutionary algorithms. Case study: Aliabad Katoul power plant. International Journal of Exergy. 2017;22(3):279-307.
• [15] Rao RR, Shrinivasa U, Srinivasan J. Synthesis of Cost-Optimal Shell-and-Tube Heat Exchangers. Heat Transfer Engineering. 1991;12(3):47-55.
• [16] Caputo AC, Pelagagge PM, Salini P. Heat exchanger design based on economic optimisation. Applied Thermal Engineering. 2008;28(10):1151-9.
• [17] Fesanghary M, Damangir E, Soleimani I. Design optimization of shell and tube heat exchangers using global sensitivity analysis and harmony search algorithm. Applied Thermal Engineering. 2009;29(5):1026-31.
• [18] Özçelik Y. Exergetic optimization of shell and tube heat exchangers using a genetic based algorithm. Applied Thermal Engineering. 2007;27(11):1849-56.
• [19] Ponce-Ortega JM, Serna-González M, Jiménez-Gutiérrez A. Use of genetic algorithms for the optimal design of shell-and-tube heat exchangers. Applied Thermal Engineering. 2009;29(2):203-9.
• [20] Ponce-Ortega JM, Serna-González M, Salcedo-Estrada LI, Jiménez-Gutiérrez A. Minimum-Investment Design of Multiple Shell and Tube Heat Exchangers Using a MINLP Formulation. Chemical Engineering Research and Design. 2006;84(10):905-10.
• [21] Ravagnani MASS, Caballero JA. Optimal heat exchanger network synthesis with the detailed heat transfer equipment design. Computers & Chemical Engineering. 2007;31(11):1432-48.
• [22] Bejan A, Tsatsaronis G, Moran MJ. Thermal design and optimization. 1996.
• [23] Johannessen E, Nummedal L, Kjelstrup S. Minimizing the entropy production in heat exchange. International Journal of Heat and Mass Transfer. 2002;45(13):2649-54.
• [24] Sun S-y, Lu Y-d, Yan C-q. Optimization in calculation of shell-tube heat exchanger. International Communications in Heat and Mass Transfer. 1993;20(5):675-85.
• [25] Allen B, Savard-Goguen M, Gosselin L. Optimizing heat exchanger networks with genetic algorithms for designing each heat exchanger including condensers. Applied Thermal Engineering. 2009;29(16):3437-44.
• [26] Guo J, Cheng L, Xu M. Optimization design of shell-and-tube heat exchanger by entropy generation minimization and genetic algorithm. Applied Thermal Engineering. 2009;29(14):2954-60.
• [27] Hilbert R, Janiga G, Baron R, Thévenin D. Multi-objective shape optimization of a heat exchanger using parallel genetic algorithms. International Journal of Heat and Mass Transfer. 2006;49(15):2567-77.
• [28] Liu Z, Cheng H. Multi-objective optimization design analysis of primary surface recuperator for microturbines. Applied Thermal Engineering. 2008;28(5):601-10.
• [29] Ozkol I, Komurgoz G. Determination of the Optimum Geometry of the Heat Exchanger Body Via A Genetic Algorithm. Numerical Heat Transfer, Part A: Applications. 2005;48(3):283-96.
• [30] Selbaş R, Kızılkan Ö, Reppich M. A new design approach for shell-and-tube heat exchangers using genetic algorithms from economic point of view. Chemical Engineering and Processing: Process Intensification. 2006;45(4):268-75.
• [31] Liu C, Bu W, Xu D. Multi-objective shape optimization of a plate-fin heat exchanger using CFD and multi-objective genetic algorithm. International Journal of Heat and Mass Transfer. 2017;111:65-82.
• [32] Lu B, Wu J, Liang Z, Liu C. Circuitry arrangement optimization for multi-tube phase change material heat exchanger using genetic algorithm coupled with numerical simulation. Energy Conversion and Management. 2018;175:213-26.
• [33] Patel VK, Rao RV. Design optimization of shell-and-tube heat exchanger using particle swarm optimization technique. Applied Thermal Engineering. 2010;30(11):1417-25.
• [34] Dastmalchi M, Sheikhzadeh GA, Arefmanesh A. Optimization of micro-finned tubes in double pipe heat exchangers using particle swarm algorithm. Applied Thermal Engineering. 2017;119:1-9.
• [35] Şencan Şahin A, Kılıç B, Kılıç U. Design and economic optimization of shell and tube heat exchangers using Artificial Bee Colony (ABC) algorithm. Energy Conversion and Management. 2011;52(11):3356-62.
• [36] Zarea H, Kashkooli FM, Soltani M, Rezaeian M. A novel single and multi-objective optimization approach based on Bees Algorithm Hybrid with Particle Swarm Optimization (BAHPSO): Application to thermal-economic design of plate fin heat exchangers. International Journal of Thermal Sciences. 2018;129:552-64.
• [37] Azad AV, Amidpour M. Economic optimization of shell and tube heat exchanger based on constructal theory. Energy. 2011;36(2):1087-96.
• [38] Rao RV, Patel V. Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Applied Mathematical Modelling. 2013;37(3):1147-62.
• [39] Fettaka S, Thibault J, Gupta Y. Design of shell-and-tube heat exchangers using multiobjective optimization. International Journal of Heat and Mass Transfer. 2013;60:343-54.
• [40] Wildi‐Tremblay P, Gosselin L. Minimizing shell‐and‐tube heat exchanger cost with genetic algorithms and considering maintenance. International Journal of Energy Research. 2007;31(9):867-85.
• [41] Lv J, Jiang X, He G, Xiao W, Li S, Sengupta D, et al. Economic and system reliability optimization of heat exchanger networks using NSGA-II algorithm. Applied Thermal Engineering. 2017;124:716-24.
• [42] Yang H, Wen J, Wang S, Li Y. Thermal design and optimization of plate-fin heat exchangers based global sensitivity analysis and NSGA-II. Applied Thermal Engineering. 2018;136:444-53.
• [43] Rao RV, Patel VK. Thermodynamic optimization of cross flow plate-fin heat exchanger using a particle swarm optimization algorithm. International Journal of Thermal Sciences. 2010;49(9):1712-21.
• [44] Rao RV, Saroj A. Constrained economic optimization of shell-and-tube heat exchangers using elitist-Jaya algorithm. Energy. 2017;128:785-800.
• [45] Rao RV, Saroj A. Economic optimization of shell-and-tube heat exchanger using Jaya algorithm with maintenance consideration. Applied Thermal Engineering. 2017;116:473-87.
• [46] Vasconcelos Segundo EHd, Amoroso AL, Mariani VC, Coelho LdS. Economic optimization design for shell-and-tube heat exchangers by a Tsallis differential evolution. Applied Thermal Engineering. 2017;111:143-51.
• [47] Majid Etghani M, Amir Hosseini Baboli S. Numerical investigation and optimization of heat transfer and exergy loss in shell and helical tube heat exchanger. Applied Thermal Engineering. 2017;121:294-301.
• [48] Raja BD, Jhala RL, Patel V. Many-objective optimization of shell and tube heat exchanger. Thermal Science and Engineering Progress. 2017;2:87-101.
• [49] Raja BD, Jhala RL, Patel V. Thermal-hydraulic optimization of plate heat exchanger: A multi-objective approach. International Journal of Thermal Sciences. 2018;124:522-35.
• [50] Raja BD, Patel V, Jhala RL. Thermal design and optimization of fin-and-tube heat exchanger using heat transfer search algorithm. Thermal Science and Engineering Progress. 2017;4:45-57.
• [51] Turgut O. Thermal and Economical Optimization of a Shell and Tube Evaporator Using Hybrid Backtracking Search - Sine Cosine Algorithm. Arabian Journal for Science and Engineering. 2017;42.
• [52] Kern DQ. Process heat transfer: Tata McGraw-Hill Education; 1950.
• [53] Gungor KE, Winterton R. A general correlation for flow boiling in tubes and annuli. International Journal of Heat and Mass Transfer. 1986;29(3):351-8.
• [54] Cooper M. Heat flow rates in saturated nucleate pool boiling-a wide-ranging examination using reduced properties. Advances in heat transfer. 16: Elsevier; 1984. p. 157-239 10.1016/s0065-2717(08)70205-3.
• [55] Coello CAC, Lechuga MS, editors. MOPSO: a proposal for multiple objective particle swarm optimization. Proceedings of the 2002 Congress on Evolutionary Computation CEC'02 (Cat No02TH8600); 2002 12-17 May 2002.
• [56] Daily and monthly reports of Compression Chillers of Tehran oil refinery. National Iranian Oil Company; 2017.