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Applying artificial neural network to approximate and predict the transient dynamic behavior of CO2 combined cooling and power cycle

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  • He, Jintao
  • Shi, Lingfeng
  • Tian, Hua
  • Wang, Xuan
  • Sun, Xiaocun
  • Zhang, Meiyan
  • Yao, Yu
  • Shu, Gequn

Abstract

The CO2 combined cooling and power cycle (CCP) is a promising alternative for waste heat recovery due to its environmental friendliness and excellent performance. However, the transient dynamic behavior analysis and control of CCP systems are challenged by the instability of waste heat sources. In transient dynamic modeling, artificial neural networks, with their nonlinear mapping capabilities and relatively low computational requirements, prove advantageous over dynamic simulation models. In this study, six commonly used artificial neural network architectures are employed for approximating and predicting the transient dynamic behavior of CCP systems and subjected to preliminary applications. Results show that the multilayer feedforward neural network is the most suitable among the six networks for predicting and approximating the CCP system's transient dynamic behavior. Based on this model, a trajectory optimization control strategy is designed, leading to a 5.3 % improvement in CCP net power. This research underscores the effectiveness of artificial neural networks in the field of CCP dynamic modeling, offering valuable guidance for its application.

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  • He, Jintao & Shi, Lingfeng & Tian, Hua & Wang, Xuan & Sun, Xiaocun & Zhang, Meiyan & Yao, Yu & Shu, Gequn, 2023. "Applying artificial neural network to approximate and predict the transient dynamic behavior of CO2 combined cooling and power cycle," Energy, Elsevier, vol. 285(C).
  • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223028451
    DOI: 10.1016/j.energy.2023.129451
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    References listed on IDEAS

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    1. Wang, Xuan & Shu, Gequn & Tian, Hua & Wang, Rui & Cai, Jinwen, 2020. "Dynamic performance comparison of different cascade waste heat recovery systems for internal combustion engine in combined cooling, heating and power," Applied Energy, Elsevier, vol. 260(C).
    2. Pan, Lisheng & Li, Bing & Shi, Weixiu & Wei, Xiaolin, 2019. "Optimization of the self-condensing CO2 transcritical power cycle using solar thermal energy," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. He, Jintao & Zhang, Yonghao & Tian, Hua & Wang, Xuan & Li, Ligeng & Cai, Jinwen & Shi, Lingfeng & Shu, Gequn, 2022. "Dynamic performance of a multi-mode operation CO2-based system combining cooling and power generation," Applied Energy, Elsevier, vol. 312(C).
    4. Zhao, Dongpeng & Han, Changho & Cho, Wonhee & Zhao, Li & Kim, Yongchan, 2022. "Directly combining a power cycle and refrigeration cycle: Method and case study," Energy, Elsevier, vol. 259(C).
    5. Cai, Jinwen & Tian, Hua & Wang, Xuan & Wang, Rui & Shu, Gequn & Wang, Mingtao, 2021. "A calibrated organic Rankine cycle dynamic model applying to subcritical system and transcritical system," Energy, Elsevier, vol. 237(C).
    6. Palagi, Laura & Pesyridis, Apostolos & Sciubba, Enrico & Tocci, Lorenzo, 2019. "Machine Learning for the prediction of the dynamic behavior of a small scale ORC system," Energy, Elsevier, vol. 166(C), pages 72-82.
    7. Huang, Z.F. & Wan, Y.D. & Soh, K.Y. & Islam, M.R. & Chua, K.J., 2022. "Off-design and flexibility analyses of combined cooling and power based liquified natural gas (LNG) cold energy utilization system under fluctuating regasification rates," Applied Energy, Elsevier, vol. 310(C).
    8. Shi, Lingfeng & Tian, Hua & Shu, Gequn, 2020. "Multi-mode analysis of a CO2-based combined refrigeration and power cycle for engine waste heat recovery," Applied Energy, Elsevier, vol. 264(C).
    9. Huang, Junwei & Xiao, Qingtai & Liu, Jingjing & Wang, Hua, 2019. "Modeling heat transfer properties in an ORC direct contact evaporator using RBF neural network combined with EMD," Energy, Elsevier, vol. 173(C), pages 306-316.
    10. Li, Ligeng & Tian, Hua & Shi, Lingfeng & Wang, Jingyu & Li, Min & Shu, Gequn, 2021. "Adaptive flow assignment for CO2 transcritical power cycle (CTPC): An engine operational profile-based off-design study," Energy, Elsevier, vol. 225(C).
    11. He, Jintao & Shi, Lingfeng & Tian, Hua & Wang, Xuan & Zhang, Yonghao & Zhang, Meiyan & Yao, Yu & Cai, Jinwen & Shu, Gequn, 2022. "Control strategy for a CO2-based combined cooling and power generation system based on heat source and cold sink fluctuations," Energy, Elsevier, vol. 257(C).
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