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Research Papers: Heat Exchangers

Cost and Entropy Generation Minimization of a Cross-Flow Plate Fin Heat Exchanger Using Multi-Objective Genetic Algorithm

[+] Author and Article Information
Pouria Ahmadi

Department of Mechanical Engineering, Sharif University of Technology, 11155–9567, Tehran, Iranpouryaahmadi81@gmail.com

Hassan Hajabdollahi

Department of Mechanical Engineering, Iran University of Science and Technology (IUST), 11155–9567, Tehran, Iranhassan.hajabdollahi@gmail.com

Ibrahim Dincer1

Department of Mechanical Engineering, Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, ON, L1H 7K4, Canadaibrahim.dincer@uoit.ca

1

Corresponding author.

J. Heat Transfer 133(2), 021801 (Nov 03, 2010) (10 pages) doi:10.1115/1.4002599 History: Received December 15, 2009; Revised March 18, 2010; Published November 03, 2010; Online November 03, 2010

In the present work, a thermal modeling is conducted for optimal design of compact heat exchangers in order to minimize cost and entropy generation. In this regard, an εNTU method is applied for estimation of the heat exchanger pressure drop, as well as effectiveness. Fin pitch, fin height, fin offset length, cold stream flow length, no-flow length, and hot stream flow length are considered as six decision variables. Fast and elitist nondominated sorting genetic algorithm (i.e., nondominated sorting genetic algorithm II) is applied to minimize the entropy generation units and the total annual cost (sum of initial investment and operating and maintenance costs) simultaneously. The results for Pareto-optimal front clearly reveal the conflict between two objective functions, the number of entropy generation units and the total annual cost. It reveals that any geometrical changes, which decrease the number of entropy generation units, lead to an increase in the total annual cost and vice versa. Moreover, for prediction of the optimal design of the plate fin heat exchanger, an equation for the number of entropy generation units versus the total annual cost is derived for the Pareto curve. In addition, optimization of heat exchangers based on considering exergy destruction revealed that irreversibilities, such as pressure drop and high temperature difference between cold and hot streams, play a key issue in exergy destruction. Thus, more efficient heat exchanger leads to have a heat exchanger with higher total cost rate. Finally, the sensitivity analysis of change in the optimum number of entropy generation units and the total annual cost with change in the decision variables of the plate fin heat exchanger is also performed, and the results are reported.

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Figures

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Figure 1

Plate fin heat exchanger

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Figure 2

Typical rectangular offset strip fin core

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Figure 3

The schematic diagram for the controlled elitist nondominated sorted multi-objective genetic algorithm with historical archive

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Figure 4

The schematic diagram of a tile furnace with a plate fin heat exchanger as preheater

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Figure 5

The distribution of Pareto-optimal points solution for entropy generation units and annual cost using NSGA-II

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Figure 6

Distribution of annual cost versus effectiveness for points of Pareto front in Fig. 5 using NSGA-II

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Figure 7

The distribution of Pareto-optimal points solution for the exergy destruction and exergy efficiency versus annual cost using NSGA-II

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Figure 8

Scattering of variables for the Pareto-optimal front in Fig. 5: (a) fin pitch, (b) fin height, (c) fin offset length, (d) cold stream flow length, (e) no-flow length, and (f) hot stream flow length

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Figure 9

The variation of number of entropy generation units with annual cost for six optimum design parameters in five cases of A–E in Fig. 5: (a) fin pitch, (b) fin height, (c) fin offset length, (d) cold stream flow length, (e) no-flow length, and (f) hot stream flow length

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