Gravitational search algorithm for economic optimization design of a shell and tube heat exchanger

Mohanty, Dillip K

doi:10.1016/j.applthermaleng.2016.06.133

Cited by 51 publications

(10 citation statements)

References 36 publications

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“…38 which works on similar principles of swarm intelligence and has been used in quite a few applications since its inception. 39,40 Mohonty 41 used gravitational search algorithm (GSA) to obtain an optimal parameter setting for a shell and tube heat exchanger to reduce the total cost of the exchanger. In the above-mentioned work, 23–28% of reduction in total cost has been obtained.…”

Section: Introductionmentioning

confidence: 99%

Application of metaheuristic algorithms in optimum thermal design analysis of a rectangular porous fin subjected to both insulated and convective tip conditions

Deshamukhya

Bhanja

Nath

2020

Proceedings of the Institution of Mechanical Engineers, Part A:

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In this novel optimization analysis, a rectangular porous fin subjected to convective tip conditions along with insulated end condition is studied. The aim is to optimize the important variables responsible for transferring heat from fin with insulated as well as convective tip to the surrounding in order to get a higher rate of heat transfer from the fin surface. Temperature-dependent solid and fluid thermal conductivities as well as variable internal heat generation is considered. To obtain heat dissipation rate through porous fin, the nonlinear governing equation is first solved analytically using Adomian decomposition method. Then the heat transfer rate, i.e., objective function of this problem is optimized with three powerful metaheuristic techniques, namely firefly algorithm, particle swarm optimization and gravitational search algorithm. A comparative analysis has revealed that fins with convective end show significantly higher heat transfer rate compared to their insulated end counterparts. Particle swarm optimization showed marginally higher heat transfer values compared to firefly algorithm but firefly algorithm took less computational effort to reach the optimum value.

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Section: Introductionmentioning

confidence: 99%

Application of metaheuristic algorithms in optimum thermal design analysis of a rectangular porous fin subjected to both insulated and convective tip conditions

Deshamukhya

Bhanja

Nath

2020

Proceedings of the Institution of Mechanical Engineers, Part A:

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“…Many optimization methods including particle swarm optimization (PSO) have been used to solve the optimization problem in design of STHE and found to have considerable performance for the method [8,9]. Multi-objective optimization techniques were used to improve the design with the objective of both economic and efficiency [10]. In this research, the multi-objective optimization of BFA is used for the STHE with various parameter designs.…”

Section: Introductionmentioning

confidence: 99%

Efficiency and economic optimization of shell and tube heat exchanger using bacteria foraging algorithm

Sai

Rao

2019

SN Appl. Sci.

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Shell and tube heat exchanger (STHE) is widely used in the industries for various purposes due to its capacity to withstand pressure. STHE has the drawback of more cost and lower efficiency. Many existing methods involve in applying the optimization technique to decrease the cost of the system. Existing method has the lower efficiency in the optimization due to the random initialization and poor convergence. This research applies the bacteria foraging algorithm (BFA) to increase the efficiency and decrease the cost of the STHE system. The BFA has the advantages of the faster convergence and the global search approach to increase the performance in design. The proposed BFA is evaluated in the two case studies and compared with the existing method. The proposed BFA in multi-objective optimization of STHE has the total cost of 41,464 €, while existing method has the total cost of 41,913 €. The proposed BFA method also achieved high heat efficiency coefficient of 1089, and the existing method has 1031.47.

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“…Particle swarm optimization is another widely used metaheuristic optimizer in order to optimize shell and tube heat exchangers in terms of thermo-economic considerations (Patel and Rao, 2011). Additionally, there has been many attempts to model and design various type of heat exchangers through relatively new emerged optimizers such as Cuckoo Search (Asadi et al, 2014), Biogeography Based Optimization , Imperialist Competitive Algorithm , Firefly Algorithm (Mohanty, 2016a), Gravitational Search Algorithm (Mohanty, 2016b), Teaching Learning Based Optimization (Patel and Savsani, 2014;Rao and Patel, 2013;Rao and Waghmare, 2015). In this study, it is aimed to determine the optimal design parameters of plate frame heat exchangers through utilizing the promising merits of Global Best Algorithm (GBEST) (Turgut and Coban, 2016).…”

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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Gravitational search algorithm for economic optimization design of a shell and tube heat exchanger

Cited by 51 publications

References 36 publications

Application of metaheuristic algorithms in optimum thermal design analysis of a rectangular porous fin subjected to both insulated and convective tip conditions

Application of metaheuristic algorithms in optimum thermal design analysis of a rectangular porous fin subjected to both insulated and convective tip conditions

Efficiency and economic optimization of shell and tube heat exchanger using bacteria foraging algorithm

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

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