Thermodynamic analysis and multi-objective optimization of a waste heat recovery system with a combined supercritical/transcritical CO2 cycle. (15th February 2023)

• Record Type: Journal Article
• Authors: Qin, Lei; Xie, Gongnan; Ma, Yuan; Li, Shulei
• Is Part Of: Energy. Volume 265(2023)
• Publication Date: 2023-02-15
• Subjects: Waste heat recovery -- S–CO2 recompression Brayton cycle -- Transcritical CO2 refrigeration cycle -- Exergy and economy analysis -- Multi-objective optimization; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.126332
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This study firstly develops a novel combined cycle system consisting of a supercritical CO2 recompression Brayton cycle and a transcritical CO2 refrigeration cycle to recover waste heat from a marine turbine for both power generation and refrigeration. The pressure drop of the total heat exchanger in the whole cycle is considered in the thermodynamic and economic modeling process. Then, the influence of crucial system parameters on the system performance and economics is investigated through parametric sensitivity analysis. Finally, the system is optimized parametrically by multi-objective optimization. The results show that the higher inlet pressure of the high-pressure compressor helps to improve the thermal efficiency but reduces the exergy efficiency; the low-temperature heat exchanger plays a decisive role in the overall system exergy destruction; the HRHE accounts for a more significant proportion of the system cost, and the pressure drop has the most significant impact on the network of the system. The optimal solution is COP = 3.059, LCOE = 18.348$/(kW/h), and η Whr = 0.651 when waste heat recovery efficiency, COP, and LCOE are considered optimization objectives. Highlights: A new combined Supercritical/Transcritical CO2 cycle is proposed for waster heat recovery. Effects of heat exchanger pressure drop and parameter sensitivity are analyzed. Thermal efficiency with reducing exergy efficiency of the combined cycle was assessed. Genetic algorithm and TOPSIS methods are applied for optimization of the combined cycle.