Abstract
Combined cogeneration systems are employed to recover waste heat from the sCO2 cycle in nuclear power generation systems. However, dealing with their multidimensional and nonlinear design and optimization spaces is a complex challenge for designers. In this study, the combined cogeneration system is parameterized and modeled: 4E models including energetic, exergetic, economic and environmental are established for sCO2 combined Organic Rankine Cycle (sCO2/ORC) and trans-critical CO2 Cycle (sCO2/tCO2) respectively. Several decision variables and five performance indicators are selected. To gain a deeper understanding of the mechanisms and optimize the combined cogeneration system, pattern recognition methods including the Self-Organizing Map (SOM), an unsupervised competitive learning approach, and Statistical Sobol Global Sensitivity Analysis methods are proposed. These techniques help identify key parameters and system patterns within the design space. Then, the Non-Dominated Sorting Whale Optimization Algorithm (NSWOA) is adopted to build a Pareto-optimal decision space, and the SOM and PCPs are proposed to visualize and mining based on pattern recognition and graph visualization interaction. The design parameters of the top cycle, P2, PRc, and x, have been confirmed as having a substantial impact on the system's performance. The multiple attributes, criterions and objectives problems in the combined cogeneration system are ultimately simplified to “Economy-Environment”, double objectives problem, and three trade-offs’ solutions are discussed: prioritizing economics, prioritizing the environment, and finding a balanced economic-environmental compromise. Pattern recognition method provides decision-makers with a fresh perspective for gaining a deeper understanding of the multi-criteria objectives space and potential trade-offs. The integration of pattern recognition into the thermodynamic, economic, and environmental models outlined in this paper represents a substantial and valuable advancement in the analysis and optimization of combined cogeneration cycles.
Published Version
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