Investigation of leakage reinjection system for supercritical CO2 power cycle using heat pump. (December 2019)

• Record Type: Journal Article
• Authors: Muhammad, Hafiz Ali; Lee, Beomjoon; Lee, Gilbong; Cho, Junhyun; Baik, Young-Jin
• Is Part Of: Renewable energy. Volume 144(2019)
• Page Start: 97
• Page End: 106
• Publication Date: 2019-12
• Subjects: Supercritical carbon dioxide (sCO2) -- Heat pump -- Leakage reinjection -- Optimization -- Genetic algorithm (GA); Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2018.10.059
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Supercritical carbon dioxide power cycle (sCO2 ) has recently attracted a great deal of interest owing to its compact size and potential for achieving high efficiency over a wide temperature range. However, several challenges still need to be overcome before the sCO2 cycle can be commercialized. One such challenge is leakage at the rotor components. The present paper discusses an innovative heat-pump application that can be used for leakage reinjection. The unique consideration of this leakage supplement system for the supercritical CO2 cycle stems from the high energy density of sCO2 and the high rotational speeds seen in turbomachinery. This paper proposes a heat-pump system that collects CO2 leakage at the turbine and liquefies this gas at the evaporator. The liquefied CO2 is then pressurized to the high pressure required for the main power generating cycle, and subsequently heat from the heat-pump working fluid is transferred to the CO2 in the heat-pump condenser. This heat-pump system offers superior compression performance over conventional methods of reinjection. Thermodynamic analysis reveals that the performance of the heat-pump system is sensitive to the saturation temperature of CO2 in the evaporator and superheating at the heat-pump's compressor inlet. Then, the genetic algorithm optimization module in MATLAB is used to optimize the system for net power consumption. Various heat-pump working fluids are investigated; R290 (Propane) delivers the best performance at 38.9% reduction in net power compared to a base case.