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Figure 6.68: Results of all evaluations during 300 generations using genetic algorithm. A clear approximation of the Pareto front is visible on the lower part of the figure. Nevertheless, in this case, the Pareto optimum frontier exhibits weak equilibrium i.e., a small change in exergy efficiency from varying the operating parameters causes a large variation in the total cost rate of product. Therefore, the ideal point cannot be utilized for decision-making in this problem. In selection of the final optimum point, it is desired to achieve a better magnitude for each objective than its initial value for the base case problem. Note that in multi- objective optimization and the Pareto solution, each point can be utilized as the optimized point. Therefore, the selection of the optimum solution depends on the preferences and criteria of the decision maker, suggesting that each may select a different point as for the optimum solution depending on his/her needs. Table 6.10 shows all the design parameters for points A-C. Table 6.10: Optimized values for design parameters of the system based on multi-objective optimization Design parameter ̇ (kg/s) Tin,Pump (oC) Pmain (kPa) Tmain (oC) PP (oC) T p TEVP (oC) A B C 0.20 0.2 0.21 115 114 114 1500 2049 3818 320 400 398.5 12 10.3 10.6 0.80 0.90 0.9 0.82 0.84 0.85 5.2 7 6.9 178PDF Image | Energy Systems for Multigeneration Purposes
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