The thermal behavior and exergy performance in terms of exergy efficiency and total exergy stored of particle-dispersed PCM units are compared with those of pure PCM unit and nanoparticles composites.
During the course of optimization, overall exergy efficiency is considered as an objective function and different design parameters of the proposed module have been optimized.
Finally, the tri-objective optimization results (exergy efficiency, thermal efficiency, and cost of each kWh) in the three methods NSGA-II, MOPSO, and MOEA/D were provided and analyzed.
Regarding the previous studies, the innovations of the current study are: using new equations to calculate the cost of the equipment installed in an organic Rankine cycle; tri-objective optimization (cost, exergy efficiency, and thermal efficiency) by changing the five design variables; using three optimization methods NSGA-II, MOPSO, and MOEA/D to compare the results of these three methods.
using new equations to calculate the cost of the equipment installed in an organic Rankine cycle; tri-objective optimization (cost, exergy efficiency, and thermal efficiency) by changing the five design variables; using three optimization methods NSGA-II, MOPSO, and MOEA/D to compare the results of these three methods.
For the first scenario, the objective functions which are utilized in the optimization study are exergy efficiency and the sum of the unit costs of the system products.
For the parametric optimization, thermal efficiency, exergy efficiency and specific work are selected as the objective functions, so the mentioned parameters are calculated for different values of the outlet pressures from the second and third pumps by using EES Engineering Equation Solverr) software.
The optimization results revealed that at the final optimal design point, the value of the exergy efficiency of the cogeneration system based on IR-SOFC is about 9.6% more than that of the system based on ER-SOFC.
This designs are the result of the exergy efficiency optimization by means of a validated Monte Carlo Ray Trace code and analytic methods.
Moreover, to identify the optimal values of the design parameters, including the evaporator 1 pressure, evaporator 2 pressure, seawater salinity and volumetric flow rate of the fresh water, multi-objective optimization with respect to the maximization of the exergy efficiency and minimization of the total product unit cost of the system is carried out.
