Supercritical CO2 Brayton cycles (SCO2BC) offer the potential of better economy and higher practicability due to their high power conversion efficiency, moderate turbine inlet temperature, compact size as compared with some traditional working fluids cycles. In this paper, the SCO2BC including the SCO2 single-recuperated Brayton cycle (RBC) and recompression recuperated Brayton cycle (RRBC) are considered, and flexible thermodynamic and economic modeling methodologies are presented. The influences of the key cycle parameters on thermodynamic performance of SCO2BC are studied, and the comparative analyses on RBC and RRBC are conducted. Based on the thermodynamic and economic models and the given conditions, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used for the Pareto-based multi-objective optimization of the RRBC, with the maximum exergy efficiency and the lowest cost per power ($/kW) as its objectives. In addition, the Artificial Neural Network (ANN) is chosen to establish the relationship between the input, output, and the key cycle parameters, which could accelerate the parameters query process.
It is observed in the thermodynamic analysis process that the cycle parameters such as heat source temperature, turbine inlet temperature, cycle pressure ratio, and pinch temperature difference of heat exchangers have significant effects on the cycle exergy efficiency. And the exergy destruction of heat exchanger is the main reason why the exergy efficiency of RRBC is higher than that of RBC under the same cycle conditions. Compared with the two kinds of SCO2BC, RBC has a cost advantage from economic perspective, while RRBC has a much better thermodynamic performance, and could rectify the temperature pinching problem that exists in RBC. Therefore, RRBC is recommended in this paper. Furthermore, the Pareto front curve between the cycle cost/ cycle power (CWR) and the cycle exergy efficiency is obtained by multi-objective optimization, which indicates that there is a conflicting relation between them. The optimization results could provide an optimum trade-off curve enabling cycle designers to choose their desired combination between the efficiency and cost. Moreover, the optimum thermodynamic parameters of RRBC can be predicted with good accuracy using ANN, which could help the users to find the SCO2BC parameters fast and accurately.