A comprehensive thermodynamic performance assessment of CO2 liquefaction and pressurization system using a heat pump for carbon capture and storage (CCS) process. (15th February 2020) - Muhammad, Hafiz Ali; Roh, Chulwoo; Cho, Jongjae; Rehman, Zabdur; Sultan, Haider; Baik, Young-Jin; Lee, Beomjoon
Graphical abstract: Highlights: An innovative CO2 compression configuration for CCS process is explored. Near critical point pressurization is vital for optimizing CO2 pressurization routes. The minimum power consumption always corresponds to minimum exergy destruction. The optimized design using Genetic Algorithm results in 7.77% saving in power. Abstract: CO2 compression process significantly contributes to the overall efficiency penalty resulting from carbon capture and storage (CCS) process. In this study, heat-pump (HP)-assisted CO2 compression configurations are examined using first and second laws of thermodynamics to reduce power consumption during CO2 compression. The performance is quantified in terms of net electric power consumption and compared with the conventional multi-stage compression. The input boundary conditions required for the proposed configurations modeling such as captured CO2 pressure, CO2 required pressure, the number of stages or the pressure ratio during CO2 compression, and cooling temperature depend on the plant configuration, location, and compression chain characteristics. This study emphasizes that the variability in boundary conditions can significantly impact the optimum thermodynamic route of CO2 pressurization. A thorough parametric investigation is thus performed to clarify the impact of these parameters on the overall power consumption. CO2 pumping or compression near the critical point was shown to play a key role in optimizing CO2 pressurization routes. Additionally, a high CO2 captured pressure and a low target pressure, number of stages, and cooling temperature were shown to enhance system performance. Furthermore, the second law analysis illustrated that the point of minimum net power consumption corresponds to the minimum exergy destruction. Finally, the optimization of the proposed system using a genetic algorithm allowed for a 7.77% electric power saving and 68.02% exergetic efficiency using the proposed system.