Deep cascade utilization of nuclear residual heat: A hybrid approach combining thermodynamic analysis and pattern recognition

Abstract

The efficient utilization of nuclear residual heat remains a critical challenge for enhancing the energy, economic, and environmental performance of nuclear power plants. Addressing this, the present study proposes a hybrid approach that combines advanced thermodynamic analysis with pattern recognition techniques to analyze and optimize the deep cascade utilization of nuclear waste heat. An integrated system is developed, incorporating the sCO₂ Brayton cycle, an ammonia-water absorption heat pump (AHP), and an ejector refrigeration cycle, to convert waste heat into heating, cooling, and power. Thermodynamic performance is assessed using the first and second laws of thermodynamics, with results indicating net work output, cooling capacity, and heating capacity of 203,901.37 kW, 333,758.08 kW, and 32,817.04 kW, respectively. The system achieves a thermal efficiency of 34.15 %, an exergy efficiency of 47.29 %, with the sCO₂ Brayton cycle contributing 78.61 % of the total exergy destruction. Complementing the thermodynamic analysis, pattern recognition techniques, including the Self-Organizing Map (SOM) and Global Sensitivity Analysis (GSA), are employed to identify key parameters and decouple complex thermodynamic relationships within the high-dimensional design space. Furthermore, the Non-Dominated Sorting Whale Optimization Algorithm (NSWOA) is applied to construct a Pareto-optimal decision space, providing optimal strategies for waste heat recovery. This study demonstrates the efficacy of this approach in visualizing intricate system dynamics, uncovering pivotal parameters, and achieving preferable system performance. These findings underscore the potential of the proposed hybrid approach to enhance the flexibility, efficiency, and sustainability of nuclear residual heat multi-generation systems.