This paper reports an investigation into the effects of different nanoparticles, including copper oxide, zirconium oxide, aluminum oxide, and silicon oxide nanoparticles, on thermoeconomic optimization of the gasket plate heat exchanger (GPHE). Effectiveness and total annual cost (TAC) were selected as two objective functions simultaneously. The non-dominated sorting genetic algorithm (NSGA-II) with seven design variables involving particle volumetric concentration and geometrical parameters of the GPHE was used for optimization. Results showed that TAC versus effectiveness was improved when nanoparticles were applied. The results of the optimization show that heat exchanger thermoeconomic parameters are better improved in the case of copper oxide as nanoparticles and generally followed by zirconium oxide, aluminiom oxide, silicon oxide. For example, 2.61% growth in the effectiveness and 6.8% reduction in the TAC are observed in the case of copper oxide nanoparticles compared with the case of without nanoparticles. The effectiveness and TAC decreased with an increase in the corrugation wavelength, while an enhancing in the plate length of the GPHE leads to an increase in effectiveness and TAC. Also, the results indicate that with an enhancement of the particle volumetric concentration of nanoparticles, effectiveness and TAC were increased linearly. Finally, the effect of the price of different nanoparticles on TAC was studied.

Nanofluid Gasket Plate Heat Exchanger NSGA?II Objective Function Particle Volumetric Concentration

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