The goals of this study are to design and optimize heat exchangers for 1 MW binary geothermal power system. It comprises two components i.e., preheater and evaporator. This study adopts genetic algorithm (GA) to reach an optimized outcome for both capital investment and operating expense aspects. Indeed the use of optimization tools in designing heat exchangers is not new. However, the material specification from Tubular Exchanger Manufacturers Association (TEMA) standards is not always considered in the previous studies. In this case, the design is not practical in actual application due to the availability of the materials and components size. Motivated by this issue, this study further investigates the design with and without TEMA. The design considers various design variables, i.e., tube diameter, number of tube passes, baffle spacing and tube length. A case study is further analyzed to validate the practicality of proposal model. Implications of the results are analyzed and discussed. The results show that total cost for both preheater and evaporator decrease 46.1% and 56.4%, respectively when it is compared to traditional approach without optimization tools.
JaliliradSCheraghaliMHAshtianiHAD. Optimal design of shell-and-tube heat exchanger based on particle swarm optimization technique. Journal of Applied Mechanics2015; 46: 21-29.
2.
RaoRVPatelV. Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Applied Mathematical Modelling2013; 37: 1147-1162.
3.
GuoZYLiuXBTaoWQShahRK. Effectiveness – thermal resistance method for heat exchanger design and analysis. International Journal of Heat and Mass Transfer2010; 53: 2877-2884.
4.
SelbaşRKızılkanÖReppichM. A new design approach for shell-and-tube heat exchangers using genetic algorithms from economic point of view. Chemical Engineering and Processing: Process Intensification2006; 45: 268-275.
5.
PatelVKRaoRV. Design optimization of shell-and-tube heat exchanger using particle swarm optimization technique. Applied Thermal Engineering2010; 30: 1417-1425.
6.
CaputoACPelagaggePMSaliniP. Heat exchanger design based on economic optimisation. Applied Thermal Engineering2008; 28: 1151-1159.
7.
HadidiAHadidiMNazariA. A new design approach for shell-and-tube heat exchangers using imperialist competitive algorithm (ICA) from economic point of view. Energy Conversion and Management2013; 67: 66-74.
8.
HadidiANazariA. Design and economic optimization of shell-and-tube heat exchangers using biogeography-based (BBO) algorithm. Applied Thermal Engineering2013; 51: 1263-1272.
9.
GuoJChengLXuM. Optimization design of shell-and-tube heat exchanger by entropy generation minimization and genetic algorithm. Applied Thermal Engineering2009; 29: 2954-2960.
10.
AzadAVAmidpourM. Economic optimization of shell and tube heat exchanger based on constructal theory. Energy2011; 36: 1087-1096.
11.
AminiMBazarganM. Two objective optimization in shell-and-tube heat exchangers using genetic algorithm. Applied Thermal Engineering2014; 69: 278-285.
12.
YangJFanALiuWJacobiAM. Optimization of shell-and-tube heat exchangers conforming to TEMA standards with designs motivated by constructal theory. Energy Conversion and Management2014; 78: 468-476.
13.
YangJOhS-RLiuW. Optimization of shell-and-tube heat exchangers using a general design approach motivated by constructal theory. International Journal of Heat and Mass Transfer2014; 77: 1144-1154.
14.
HadidiA. A robust approach for optimal design of plate fin heat exchangers using biogeography based optimization (BBO) algorithm. Applied Energy2015; 150: 196-210.
15.
MarianiVCDuckARKGuerraFACoelhoLDSRaoRV. A chaotic quantum-behaved particle swarm approach applied to optimization of heat exchangers. Applied Thermal Engineering2012; 42: 119-128.
16.
GhaneiAAssarehEBiglariMGhanbarzadehANoghrehabadiAR. Thermal-economic multi-objective optimization of shell and tube heat exchanger using particle swarm optimization (PSO). Heat and Mass Transfer2014; 50: 1375-1384.
17.
FesangharyMDamangirESoleimaniI. Design optimization of shell and tube heat exchangers using global sensitivity analysis and harmony search algorithm. Applied Thermal Engineering2009; 29: 1026-1031.
18.
AsadiMSongYSundenBXieG. Economic optimization design of shell-and-tube heat exchangers by a cuckoo-search-algorithm. Applied Thermal Engineering2014; 73: 1032-1040.
19.
WangZLiY. Irreversibility analysis for optimization design of plate fin heat exchangers using a multi-objective cuckoo search algorithm. Energy Conversion and Management2015; 101: 126-135.
20.
ŞahinAŞKılıçBKılıçU. Design and economic optimization of shell and tube heat exchangers using Artificial Bee Colony (ABC) algorithm. Energy Conversion and Management2011; 52: 3356-3362.
21.
EllistonBMacGillIDiesendorfM. Least cost 100% renewable electricity scenarios in the Australian National Electricity Market. Energy Policy2013; 59: 270-282.
22.
López-LezamaJMContrerasJPadilha-FeltrinA. Location and contract pricing of distributed generation using a genetic algorithm. International Journal of Electrical Power and Energy Systems2012; 36: 117-126.
23.
MohamedFAKoivoHN. Online management genetic algorithms of microgrid for residential application. Energy Conversion and Management2012; 64: 562-568.
24.
Acosta-GonzálezEFernández-RodríuezF. Forecasting financial failure of firms via genetic algorithms. Computational Economics2014; 43: 133-157.
ZamaniR. A competitive magnet-based genetic algorithm for solving the resource-constrained project scheduling problem. European Journal of Operational Research2013; 229: 552-559.
27.
XuYLiKHuJLiK. A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Information Sciences2014; 270: 255-287.
28.
KiaRKhaksar-HaghaniFJavadianNTavakkoli-MoghaddamR. Solving a multi-floor layout design model of a dynamic cellular manufacturing system by an efficient genetic algorithm. Journal of Manufacturing Systems2014; 33: 218-232.
29.
LinCChoyKLHoGTSNgTW. A genetic algorithm-based optimization model for supporting green transportation operations. Expert Systems with Applications2014; 41: 3284-3296.
D’AddonaDMTetiR. Genetic algorithm-based optimization of cutting parameters in turning processes. Procedia CIRP2013; 7: 323-328.
32.
ElsayedSMSarkerRAEssamDL. A new genetic algorithm for solving optimization problems. Engineering Applications of Artificial Intelligence2014; 27: 57-69.
33.
BergmanTLIncroperaFPDeWittDPLavineAS. Fundamentals of Heat and Mass Transfer. John Wiley and Sons, 2011.
34.
ShahRKSekulicDP. Fundamentals of Heat Exchanger Design. John Wiley and Sons, 2003.
KernDQ. Process Heat Transfer. New York: McGraw-Hill, 1965.
37.
SinnottRK. Chemical Engineering Design, in Coulson and Richardson’s Chemical Engineering. 4 ed: Butterworth-Heinemann 2005; 6.
38.
PetersMTimmerhausK. Plant Design And Economics for Chemical Engineers (fouth ed) McGraw-Hill. New York, NY, United States, 1991.
39.
TaalMBulatovIKlemešJStehlıkP. Cost estimation and energy price forecasts for economic evaluation of retrofit projects. Applied Thermal Engineering2003; 23: 1819-1835.
40.
HollandJH. Adaptation in Natural and Artificial Systems. Univ. of Mich Press, Ann Arbor, 1975.
41.
ColinRRJonathanE. Genetic Algorithms-Principles and Perspectives, A guide to GA Theory, USA: Kluwer Academic Publisher 2002; 19.
42.
FettakaSThibaultJGuptaY. Design of shell-and-tube heat exchangers using multiobjective optimization. International Journal of Heat and Mass Transfer2013; 60: 343-354.
