TRIPLE-OBJECTIVE OPTIMIZATION OF SUPERCRITICAL CO2 RECOMPRESSION BRAYTON CYCLE IN SOLAR TOWER SYSTEMS WITH ENERGY, EXERGY AND EXERGOECONOMIC ANALYSIS

This study addresses the energy, exergy and exergoeconomic analyses of the supercritical CO2 recompression Brayton cycle used in solar tower systems. In the study, a three-objective optimization model was developed using artificial neural networks (ANN) to optimize the system performance. The model provides information for the development of sustainable solar energy systems by providing analyses on key factors such as energy efficiency, environmental impact and economic viability. The results show that the supercritical CO2 cycle provides higher thermal efficiency compared to conventional systems and offers cost advantages by reducing the size of system components. In addition, the analyses show that energy and exergy losses can be minimized and the cost effectiveness of the system can be increased, providing important findings in terms of the efficiency and economic viability of solar energy systems.