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6.2.4.4 Results and discussion The genetic algorithm optimization is performed for 250 generations, using a search population size of M = 100 individuals, crossover probability of pc = 0.9, gene mutation probability of pm = 0.035 and controlled elitism value c = 0.55. The results of the optimization are given and described. 6.2.4.4.1 Optimization results Fig. 6.33 shows the Pareto frontier solution for this multigeneration system with objective functions indicated in equation. (6.1) and (6.2) in multi-objective optimization. It can be seen in this figure that the total cost rate of products increases moderately as the total exergy efficiency of the cycle increases to about 65 %. Increasing the total exergy efficiency from 65 % to 68 % increases the cost rate of product significantly. Table 6.4: Optimization constraints and their rationales Constraint Reason GTIT < 1550 K P2/P1 < 22 AC < 0.9 GT < 0.9 PHP < 40 bar PLP < 5.5 bar 10 oC < PPHP < 22 oC 12 oC < PPLP < 22 oC ST < 0.9 p < 0.9 2 oC < TEVP < 6 oC 8 kPa < PCond < 10 kPa 500 kPa < PORC < 750 kPa 180 kPa < Pex,ORC < 250 kPa 20 kPa < PEVP,ORC < 35 kPa Material temperature limit Commercial availability Commercial availability Commercial availability Commercial availability Commercial availability Heat transfer limit Heat transfer limit Commercial availability Commercial availability Cooling load limitation Thermal efficiency limit ORC commercial availability ORC commercial availability Cooling load limitation 141PDF Image | Energy Systems for Multigeneration Purposes
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