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Machine Learning for the prediction of the dynamic behavior of a small scale ORC system

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  • Palagi, Laura
  • Pesyridis, Apostolos
  • Sciubba, Enrico
  • Tocci, Lorenzo

Abstract

Dynamic modelling plays a crucial role in the analysis of Organic Rankine Cycle (ORC) systems for waste heat recovery, which deal with a highly unsteady heat source. The efficiency of small scale ORCs (i.e. below 100 kW power output) is low (<10%). Therefore, it is essential to keep the performance of the system as stable as possible. To do so, it is helpful to be able to predict the dynamic behavior of the system, in order to perform a maximization of its performance over the time.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:166:y:2019:i:c:p:72-82
    DOI: 10.1016/j.energy.2018.10.059
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    References listed on IDEAS

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    Cited by:

    1. Ying Zhang & Li Zhao & Shuai Deng & Ming Li & Yali Liu & Qiongfen Yu & Mengxing Li, 2022. "Novel Off-Design Operation Maps Showing Functionality Limitations of Organic Rankine Cycle Validated by Experiments," Energies, MDPI, vol. 15(21), pages 1-19, November.
    2. Michael Chukwuemeka Ekwonu & Mirae Kim & Binqi Chen & Muhammad Tauseef Nasir & Kyung Chun Kim, 2023. "Dynamic Simulation of Partial Load Operation of an Organic Rankine Cycle with Two Parallel Expanders," Energies, MDPI, vol. 16(1), pages 1-18, January.
    3. Lisheng Pan & Huaixin Wang, 2019. "Experimental Investigation on Performance of an Organic Rankine Cycle System Integrated with a Radial Flow Turbine," Energies, MDPI, vol. 12(4), pages 1-20, February.
    4. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Yao, Baofeng & Wang, Yan, 2022. "An outlier removal and feature dimensionality reduction framework with unsupervised learning and information theory intervention for organic Rankine cycle (ORC)," Energy, Elsevier, vol. 254(PB).
    5. Tian, Zhen & Gan, Wanlong & Zou, Xianzhi & Zhang, Yuan & Gao, Wenzhong, 2022. "Performance prediction of a cryogenic organic Rankine cycle based on back propagation neural network optimized by genetic algorithm," Energy, Elsevier, vol. 254(PB).
    6. Wan Rashidi Bin Wan Ramli & Apostolos Pesyridis & Dhrumil Gohil & Fuhaid Alshammari, 2020. "Organic Rankine Cycle Waste Heat Recovery for Passenger Hybrid Electric Vehicles," Energies, MDPI, vol. 13(17), pages 1-27, September.
    7. Imran, Muhammad & Pili, Roberto & Usman, Muhammad & Haglind, Fredrik, 2020. "Dynamic modeling and control strategies of organic Rankine cycle systems: Methods and challenges," Applied Energy, Elsevier, vol. 276(C).
    8. Zhang, Yuan & Wu, Xiaocheng & Tian, Zhen & Gao, Wenzhong & Peng, Hao & Yang, Ke, 2023. "Comparison of random forest, support vector regression, and long short term memory for performance prediction and optimization of a cryogenic organic rankine cycle (ORC)," Energy, Elsevier, vol. 280(C).

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