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Performance Analysis and Rapid Optimization of Vehicle ORC Systems Based on Numerical Simulation and Machine Learning

Author

Listed:
  • Xin Wang

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China)

  • Xia Chen

    (Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China)

  • Chengda Xing

    (Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China)

  • Xu Ping

    (Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China)

  • Hongguang Zhang

    (Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China)

  • Fubin Yang

    (Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China)

Abstract

The organic Rankine cycle (ORC) system is an important technology for recovering energy from the waste heat of internal combustion engines, which is of significant importance for the improvement of fuel utilization. This study analyses the performance of vehicle ORC systems and proposes a rapid optimization method for enhancing vehicle ORC performance. This study constructed a numerical simulation model of an internal combustion engine-ORC waste heat recovery system based on GT-Suite software v2016. The impact of key operating parameters on the performance of two organic Rankine cycles: the simple organic Rankine cycle (SORC) and the recuperative organic Rankine cycle (RORC) was investigated. In order to facilitate real-time prediction and optimization of system performance, a data-driven rapid prediction model of the performance of the waste heat recovery system was constructed based on an artificial neural network. Meanwhile, the NSGA-II multi-objective algorithm was used to investigate the competitive relationship between different performance objective functions. Furthermore, the optimal operating parameters of the system were determined by utilizing the TOPSIS method. The results demonstrate that the highest thermal efficiencies of the SORC and RORC are 6.21% and 8.61%, respectively, the highest power outputs per unit heat transfer area (POPAs) are 6.98 kW/m 2 and 8.99 kW/m 2 , respectively, the lowest unit electricity production costs (EPC) are 7.22 × 10 −2 USD/kWh and 3.15 × 10 −2 USD/kWh, respectively, and the lowest CO 2 emissions are 2.85 ton CO 2,eq and 3.11 ton CO 2,eq , respectively. The optimization results show that the RORC exhibits superior thermodynamic and economic performance in comparison to the SORC, yet inferior environmental performance.

Suggested Citation

  • Xin Wang & Xia Chen & Chengda Xing & Xu Ping & Hongguang Zhang & Fubin Yang, 2024. "Performance Analysis and Rapid Optimization of Vehicle ORC Systems Based on Numerical Simulation and Machine Learning," Energies, MDPI, vol. 17(18), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:18:p:4542-:d:1475136
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    References listed on IDEAS

    as
    1. Shu, Gequn & Zhao, Mingru & Tian, Hua & Wei, Haiqiao & Liang, Xingyu & Huo, Yongzhan & Zhu, Weijie, 2016. "Experimental investigation on thermal OS/ORC (Oil Storage/Organic Rankine Cycle) system for waste heat recovery from diesel engine," Energy, Elsevier, vol. 107(C), pages 693-706.
    2. Ziviani, Davide & James, Nelson A. & Accorsi, Felipe A. & Braun, James E. & Groll, Eckhard A., 2018. "Experimental and numerical analyses of a 5 kWe oil-free open-drive scroll expander for small-scale organic Rankine cycle (ORC) applications," Applied Energy, Elsevier, vol. 230(C), pages 1140-1156.
    3. Zhou, Jianzhao & Chu, Yin Ting & Ren, Jingzheng & Shen, Weifeng & He, Chang, 2023. "Integrating machine learning and mathematical programming for efficient optimization of operating conditions in organic Rankine cycle (ORC) based combined systems," Energy, Elsevier, vol. 281(C).
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