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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 6.16 shows all the design parameters for points A-C. Table 6.16: Optimized values for design parameters of the system based on multi-objective optimization. Design parameter A L ( m) 2 ̇ (kg/s) 100.5 PP ( oC) 5 T (%) 75 p (%) 81 TEVP (oC) 3.5 TCond ( oC) 9 ̇ ( kg/s) 0.5 b (m) 0.6 As shown in Fig. 6.94, the optimized values between 27 % and 73 %. In order to provide total cost rate, a curve is fitted on the optimized points obtained from the evolutionary algorithm. This fitted curve is shown in Fig. 6.94. The expression for this fitted curve is given as follows: ̇ (6.9) B 2.2 100.6 2.5 83 77 4 8 1.8 0.7 C 2.4 100.3 1.5 84 76 3.5 6 2 0.7 for exergy efficiency a good relationship between exergy efficiency and on the Pareto frontier range This is allowable when the efficiency varies between 0.32 and 0.75.In order to study the variation of thermodynamic characteristics, three different points (A to C) on the Pareto frontier are considered. Table 6.17 shows total cost rate of the system, the total exergy destruction, the system exergy efficiency, the cooling loads of the system fresh water and hydrogen production arte of the system. Table 6.17: Thermodynamic characteristics of three different points on the Pareto frontier. ̇Ѱ̇̇̇̇̇ Point A 29 0.28 B 52.5 0.60 C 66.6 0.73 $/h kg/h kg/h 143.8 0.10 312 (kW) kW kW 1242 40 1277 140.5 1248 146.54 153.7 0.18 161.6 0.23 720 584.7 205PDF Image | Energy Systems for Multigeneration Purposes
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