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As shown in Fig. 6.67, the optimized values for exergy efficiency on the Pareto frontier range between 31 % and 34 %. To provide a good relation between exergy efficiency and total cost rate, a curve is fitted on the optimized points obtained from the evolutionary algorithm. This fitted curve is shown in Fig. 6.33. The expression for this fitted curve is given as follows: ̇ (6.7) This is allowable when the efficiency varies between 0.29 and 0.34. To study the variation of thermodynamic characteristics, three different points (A to C) on the Pareto frontier are considered. Table 6.11 shows total cost rate of the system, the total exergy destruction, the system efficiency, the heating and cooling loads of the system and the CO2 emission of the system. Table 6. 11: Thermodynamic characteristics of three different points on the Pareto frontier. ̇ Ѱ ̇ ̇ ̇ ̇ CO2 ̇ ̇ ̇ Point A B C (kW) kW kW kW $/h 1487 271.8 1741 362 1644 874 kg/kWh kg/h kg/s kg/s 346.8 1.19 0.52 1.08 364 1.30 0.53 1.19 361.7 1.50 0.52 1.4 278.35 0.31 307.10 0.32 351.10 0.33 3749 2000 3473 1543 3477 1614 From point A to point C in this table both total cost rate of the system and exergy efficiencies increases. As already stated, point A is preferred when total cost rate is a single objective function and design point C when exergy efficiency is a single objective function. Design point B has better results for both objective functions. Other thermodynamic properties correctly confirm this trend. For instance, from point B to C, the total exergy destruction rate decreases when the exergy efficiency increases. To better understand the variations of all design parameters, the scattered distribution of the design parameters are shown in Figs. 6.69 to 6.70. The results show that ORC pump inlet temperature (Fig. 6.69b) and absorption chiller evaporator temperature (Fig. 6.70d) tend to become as high as possible. This observation means that an increase in these parameters leads to the better optimization results. For example, an increase in these design parameters leads to improvement for both objective functions in multi-objective optimization. In Figs. 6.69-6.70, we see that the ORC turbine inlet pressure (Fig. 6.69c), the ORC turbine inlet temperature (Fig. 6.69d), the evaporator pinch point temperature difference (Fig. 6.70a), ORC turbine isentropic efficiency (Fig. 6.70b), and the ORC pump isentropic efficiency (Fig. 6.70c) have scattered 179PDF Image | Energy Systems for Multigeneration Purposes
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