A new design approach for shell-and-tube heat exchangers using genetic algorithms from economic point of view

Selbaş, Reşat; Kızılkan, Önder; Reppich, Marcus

doi:10.1016/j.cep.2005.07.004

Cited by 218 publications

(83 citation statements)

References 7 publications

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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 Main mentioning

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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Section: Fig 1 Schematic Demonstration Of a Chevron Plate With Main mentioning

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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“…This section involves the programing in binary codes, implementing the functions of GA. Based on the program, array of the convection is optimized.GA programing in binary codes possesses the advantages: easy coding and decoding; feasible crossing and mututation feasible; compliane with the principle of the minimum character coding; convienent for the pattern theory for theoretical analysis and so on [4].The optimization procedure is introduced below:…”

Section: Binary Programs Of Ga and Optimization Analysismentioning

confidence: 99%

“…Its main idea lies on the simulation of mechanism of inherence and theory of evolution to search for optimization [2][3][4]. Up to now, it has been widely applied in automatic control, computation science, engineering design, intelligential fault diagnosis, management science and social science.…”

Section: Introductionmentioning

confidence: 99%

Optimization of boiler's convection tubes based on Genetic Algorithm

Zhang¹,

Zhao²,

Cheng³

et al. 2016

Proceedings of the 2016 4th International Conference on Electrical &Amp; Electronics Engineering and Computer Science (ICEEECS

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Abstract. Genetic Algorithm (GA) is created by Prof. John Holland from Michigan University, deriving from the Darwin's theory of evolution, Weizmann's theory of species selection, and Mendel's theory of inheritance. In present study, the Genetic Algorithm is introduced to optimize the boiler convection tube bank of SHL32-2.5/AI and the method of programing group for optimizationbinary code,is achieved. Constraints are considered by means of penalty functions. Based on the data obtained in our case, the result shows in the almost equal amount of heat absorbed, the structure with optimization can economize on steel by 10%. The idea of present study can also applied in economizer and other heat transfer array of tubes and can be used as a reference for further study.

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“…Some researchers focused the objective functions on decreasing total cost and heat transfer area [1][2][3]. Selbas et al [4] optimized a shell and tube heat exchanger economically using a genetic algorithm that considers as the objective function heat transfer area and shows the relationship between heat transfer area and total cost: heat transfer area increases as total cost increases. Vahdat Azad and Amidpour [5] optimized shell and tube heat exchangers based on constructed theory, with the objective of reducing the total cost of the heat exchanger.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Finned Shell and Tube Heat Exchanger Using a Multi-Objective Optimization Genetic Algorithm

2015

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Abstract:Heat transfer rate and cost significantly affect designs of shell and tube heat exchangers. From the viewpoint of engineering, an optimum design is obtained via maximum heat transfer rate and minimum cost. Here, an analysis of a radial, finned, shell and tube heat exchanger is carried out, considering nine design parameters: tube arrangement, tube diameter, tube pitch, tube length, number of tubes, fin height, fin thickness, baffle spacing ratio and number of fins per unit length of tube. The "Delaware modified" technique is used to determine heat transfer coefficients and the shell-side pressure drop. In this technique, the baffle cut is 20 percent and the baffle ratio limits range from 0.2 to 0.4. The optimization of the objective functions (maximum heat transfer rate and minimum total cost) is performed using a non-dominated sorting genetic algorithm (NSGA-II), and compared against a one-objective algorithm, to find the best solutions. The results are depicted as a set of solutions on a Pareto front, and show that the heat transfer rate ranges from 3517 to 7075 kW. Also, the minimum and maximum objective functions are specified, allowing the designer to select the best points among these solutions based on requirements. Additionally, variations of shell-side pressure drop with total cost are depicted, and indicate that the pressure drop ranges from 3.8 to 46.7 kPa. OPEN ACCESSSustainability 2015, 7 11680

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A new design approach for shell-and-tube heat exchangers using genetic algorithms from economic point of view

Cited by 218 publications

References 7 publications

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

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

Optimization of boiler's convection tubes based on Genetic Algorithm

Optimization of a Finned Shell and Tube Heat Exchanger Using a Multi-Objective Optimization Genetic Algorithm

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