Thermodynamic analysis and evolutionary algorithm based on multi-objective optimization performance of actual power generating thermal cycles

Abstract The key objective of this research is to find out the best assessment principles for irreversible power generating thermal cycles. These approaches, which can be found through previous works, are the ecological coefficient of performance, exergetic performance coefficient and maximum available work. Irreversible Carnot power cycle is defined as system. External and internal irreversibilities are encompassed in the thermodynamic analysis. In this paper, two scenarios are defined in the optimization progression. The outputs of each scenarios are studied individually. Throughout the first scenario, with the aim of maximize the ecological coefficient of performance (ECOP), the exergetic performance criteria ( E P C ) and maximum available work ( M A W ), multi-objective optimization algorithms is engaged. Furthermore, throughout the second scenario, three objective functions comprising the first law efficiency ( η ), the exergetic performance criteria ( E P C ) and maximum available work ( M A W ) are maximized at the same time via multi objective optimization approaches. The multi objective evolutionary approaches (MOEAs) coupled with non-dominated sorting genetic algorithm (NSGA-II) approach is applied in the present paper. Decision making is performed via three well-known approaches comprising LINAMP and TOPSIS and FUZZY. Finally, error analysis of the outputs are accomplished for the aforementioned system.

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