multi objective optimization of shell and tube heat exchanger with pcm based nanofluid using exergy analyses and adoptive genetic algorithm

In this research, a new design approach for shell and tube heat exchanger optimization design based on nepcm nanofluid and applying adoptive genetic algorithm has been developed. nano encapsulated phase change material (nepcm) was used as base fluid inside the shell and tube heat exchanger (sthe). a systematic optimization design approach has not been introduced for designing these nanofluid-based sthe. the exergy efficiency and cost are two important parameters in heat exchanger design. the total cost includes the capital investment for equipment (heat exchanger surface area) and operating cost (for energy expenditures related to pumping). tube. diameter, tube pitch ratio, tube number, baffle spacing ratio, nanofluid concentration as well as baffle cut ratio were considered as seven design parameters. for optimal design of a shell and tube heat exchanger, it was first thermally modeled using eentu method while belledelaware procedure was applied to estimate its shell side heat transfer coefficient and pressure drop. fast and elitist non-dominated sorting. genetic algorithm (.ga) with continuous and discrete variables were applied to obtain the maximum exergy efficiency and the minimum total cost as two objective functions. the results of optimal designs were a set of multiple optimum solutions, called ‘pareto optimal solutions. the sensitivity analysis of change in optimum effectiveness and total cost with change in design parameters of the shell and tube heat exchanger was also performed and the results are reported. the results showed that using nepcm concentration and tube number enhanced exergy efficiency around 9%, while increasing cost about 22%.