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Exergy efficiency ̇̇̇̇̇ ̇̇ Total cost rate are given elsewhere [103]. 6.4.3.2 Decision variables The following decision variables (design parameters) are selected for this study: Warm surface mass flow rate ( ̇ ), OTEC evaporator pinch point temperature (PP) difference, OTEC turbine isentropic efficiency (T), pump isentropic efficiency (p), absorption chiller evaporator temperature (TEVP), PV/T inlet air mass flow rate ( ̇ ), PV/T collector length (L), PV/T collector width (b) and OTEC condenser temperature (TCond ). Although the decision variables may be varied in the optimization procedure, each is normally required to be within a reasonable range. Such constraints, based on earlier reports are listed in Table 6.15. (6.7) (6.8) Here, Z K is the purchase cost of each component. More details about equipment purchase cost ̇ ∑̇ Table 6.15: Optimization constraints and their rationales. Rationales Due to pump required work limitation Due to heat transfer limit Due to commercial availability Due to heat transfer limit Due to commercial availability Due to commercial availability Due to cooling load limitation Due to commercial availability Due to heat transfer limit Genetic algorithms apply an iterative, stochastic search imitate in a simplified manner principles of biological evolution [93]. A characteristic of an Constraint 100< ̇ <150 3 < PP <6 oC 0.5 kg/s < ̇ 0.8 m < L< 2 m T < 0.9 p < 0.9 3oC < TEVP < 7 oC 0.4 m < b< 0.7 m 5oC < TCond < 9 oC 6.4.3.3 Evolutionary algorithm: Genetic algorithm <2 kg/s strategy to find an optimal solution and 202PDF Image | Energy Systems for Multigeneration Purposes
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