Optimization design of shell-and-tube heat exchanger by entropy generation minimization and genetic algorithm

Guo, Jiangfeng; Cheng, Lin; Xu, Mingtian

doi:10.1016/j.applthermaleng.2009.03.011

Cited by 139 publications

(50 citation statements)

References 14 publications

(22 reference statements)

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“…From the second law of thermodynamics, we know that for real processes the sum of increases in entropy in the system should be greater than zero [19,[24][25][26][27][28].ṁ…”

Section: Entropy Generation In a Condensermentioning

confidence: 99%

Entropy generation in a condenser and related correlations

Askowski¹,

Jaworski²,

Smyk³

2015

Archives of Thermodynamics

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The paper presents an analysis of relations describing entropy generation in a condenser of a steam unit. Connections between entropy generation, condenser ratio, and heat exchanger effectiveness, as well as relations implied by them are shown. Theoretical considerations allowed to determine limits of individual parameters which describe the condenser operation. Various relations for average temperature of the cold fluid were compared. All the proposed relations were verified against data obtained using a simulator and actual measurement data from a 200 MW unit condenser. Based on data from a simulator it was examined how the sum of entropy rates, steam condenser effectiveness, terminal temperature difference and condenser ratio vary with the change in the inlet cooling water temperature, mass flow rate of steam and the cooling water mass flow rate. Keywords: Steam condenser; Entropy generation

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“…From the second law of thermodynamics, we know that for real processes the sum of increases in entropy in the system should be greater than zero [19,[24][25][26][27][28].ṁ…”

Section: Entropy Generation In a Condensermentioning

confidence: 99%

Entropy generation in a condenser and related correlations

Askowski¹,

Jaworski²,

Smyk³

2015

Archives of Thermodynamics

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“…Specifically, genetic algorithm (Selbas et al, 2006;Guo et al, 2009;Fettaka et al, 2013;Ponce-Ortega et al, 2009;Khosravi et al, 2015;Amini and Bazargan, 2014) has been intensively benefited by many researchers as a favourable option for optimum thermal design. However, there has also been plenty of proposed stochastic optimization based solution strategy for modelling various type of heat exchangers most of which outperforms genetic algorithm with respect to their optimization performance.…”

Section: Fig 1 Schematic Demonstration Of a Chevron Plate With Mainmentioning

confidence: 99%

Multi-objective thermal design optimization of plate frame heat exchangers through Global Best Algorithm

Turgut¹

2017

Bitlis Eren University Journal of Science and Technology

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Plate frame heat exchangersThermal design A B S T R A C TThis study deals with thermal design of plate frame heat exchangers based on Global Best Algorithm. By utilizing some basic perturbation schemes adopted from Differential Search and Differential Evolution, Global Best Algorithm aims to obtain optimum solution of any optimization problem with intensifying on exploitation of the promising solutions rather than exploring of the unvisited paths of the search domain. Firstly, optimization performance of the proposed algorithm has been benchmarked against variety of well-known optimization algorithms by means of 16 different highly challenging optimization test functions. Then, the proposed method is put into practice to acquire the optimal values of the design variables those optimize the considered problem objectives including overall heat transfer coefficient, total cost and weight of the plate frame heat exchangers separately as well as simultaneously. Considerable improvement in objective function values is observed as compared to preliminary design in single objective manner. Pareto frontier is constructed for dual and triple objective and best optimal solution among the curve is selected by means of the widely-known decision making methods of LINMAP, TOPSIS, and Shannon's entropy theory. Optimal results obtained from each decision making theory are compared with respect to their corresponding deviation indexes and the best one is preferred. A sensitivity analysis is then performed to study the vibrational influences of some design parameters on the considered objective functions. It is observed that selected design variables has a significant effect on problem objectives.

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“…www.intechopen.com Guo et al (2009) developed a new shell-and-tube heat exchanger optimization design approach using entropy generation minimization and genetic algorithm. The researchers employed the dimensionless entropy generation rate obtained by scaling the entropy generation on the ratio of the heat transfer rate to the inlet temperature of cold fluid as the objective function.…”

Section: Optimization Of Heat Exchangersmentioning

confidence: 99%