Volume 43: Energy Transitions toward Carbon Neutrality: Part VI

Performance optimization of organic Rankine cycle systems for waste-heat recovery: Phase change heat exchanger sizing based on infinitesimal method Shasha Han, Xingtao Li, Chang He, Bingjian Zhang, Qinglin Chen



Phase change heat exchanger (PCHE) is an essential component of the integrated energy system (cooling, heating and power) modeling and optimization, where the heat transfer performance directly affects the overall performance and the equipment investment accounts for a significant proportion. This work proposes a new collaborative optimization method for coupling micro-phase change heat exchanger (MPCHE) sizing and organic Rankine cycle (ORC) systems performance to recover different grades low-temperature waste heat considering the heat transfer characteristics with fuzzy heat transfer zones, complex flow patterns and chaotic motion law. Firstly, considering the dynamic changes of thermophysical property of fluids, a new design method of the infinitesimal phase-change heat exchanger based on phase transition rate infinitesimal (PCHE-PTI) is proposed, and a thermo-hydraulic model close to the real heat transfer law is established. Then, the traditional-three-stage phase change heat exchanger design method (PCHE-TTS) and the micro-segmentation phase change heat exchanger design based on the number of baffle and tube pass (PCHE-MBT) are coupled to ORC system respectively. Finally, the reinforcement learning neural network algorithm (RLNNA) is used to solve the three mixed integer nonlinear programming models to achieve the optimal thermal-economic. The results show that the new method proposed can accurately describe the actual phase change heat transfer behavior and can also obtain an electricity production cost that is 5% lower than the traditional method and 8% lower than the original design. Furthermore, the coupling optimization process of MPCHE-ORC-Fluid is successfully realized by considering Fluid selection, and the most cost-effective optimization configuration is obtained, thereby improving the accuracy and reliability of design optimization.

Keywords Organic Rankine cycle, micro-segment heat transfer, low grade waste heat, neural network algorithm with reinforcement learning, thermo-economic analysis

Copyright ©
Energy Proceedings