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Control of superheat of organic Rankine cycle under transient heat source based on deep reinforcement learning

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  • Wang, Xuan
  • Wang, Rui
  • Jin, Ming
  • Shu, Gequn
  • Tian, Hua
  • Pan, Jiaying

Abstract

The organic Rankine cycle (ORC) is a promising technology for engine waste heat recovery. During real-world operation, the engine working condition varies frequently to satisfy the power demand; thus, the transient nature of engine waste heat presents significant control challenges for the ORC. To control the superheat of the ORC precisely under a transient heat source, several optimal control methods have been used such as model predictive control and dynamic programing. However, most of them depend strongly on the accurate prediction of future disturbances. Deep reinforcement learning (DRL) is an artificial-intelligence algorithm that can overcome the aforementioned disadvantage, but the potential of DRL in control of thermodynamic systems has not yet been investigated. Thus, this paper proposes two DRL-based control methods for controlling the superheat of ORC under a transient heat source. One directly uses the DRL agent to learn the control strategy (DRL control), and the other uses the DRL agent to optimize the parameters of the proportional–integral–derivative (PID) controller (DRL-based PID control). Additionally, a switching mechanism between different DRL controllers is proposed for improving the training efficiency and enlarging the operation range of the controller. The results of this study indicate that the DRL agent can satisfactorily perform the control task and optimize the traditional controller under the trained and untrained transient heat source. Specifically, the DRL control can track the reference superheat with an average error of only 0.19 K, whereas that of the traditional PID control is 2.16 K. Furthermore, the proposed switching DRL control exhibits excellent tracking performance with an average error of only 0.21 K and robustness over a wide range of operation conditions. The successful application of DRL demonstrates its considerable potential for the control of thermodynamic systems, providing a useful reference and motivation for the application to other thermodynamic systems.

Suggested Citation

  • Wang, Xuan & Wang, Rui & Jin, Ming & Shu, Gequn & Tian, Hua & Pan, Jiaying, 2020. "Control of superheat of organic Rankine cycle under transient heat source based on deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    • Handle: RePEc:eee:appene:v:278:y:2020:i:c:s0306261920311399
    DOI: 10.1016/j.apenergy.2020.115637
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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. Shu, Gequn & Li, Xiaoning & Tian, Hua & Liang, Xingyu & Wei, Haiqiao & Wang, Xu, 2014. "Alkanes as working fluids for high-temperature exhaust heat recovery of diesel engine using organic Rankine cycle," Applied Energy, Elsevier, vol. 119(C), pages 204-217.
    3. Wieczorek, Maciej & Lewandowski, Mirosław, 2017. "A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithm," Applied Energy, Elsevier, vol. 192(C), pages 222-233.
    4. Xuan Wang & Hua Tian & Gequn Shu, 2016. "Part-Load Performance Prediction and Operation Strategy Design of Organic Rankine Cycles with a Medium Cycle Used for Recovering Waste Heat from Gaseous Fuel Engines," Energies, MDPI, vol. 9(7), pages 1-21, July.
    5. Quoilin, Sylvain & Aumann, Richard & Grill, Andreas & Schuster, Andreas & Lemort, Vincent & Spliethoff, Hartmut, 2011. "Dynamic modeling and optimal control strategy of waste heat recovery Organic Rankine Cycles," Applied Energy, Elsevier, vol. 88(6), pages 2183-2190, June.
    6. Rech, Sergio & Zandarin, Simone & Lazzaretto, Andrea & Frangopoulos, Christos A., 2017. "Design and off-design models of single and two-stage ORC systems on board a LNG carrier for the search of the optimal performance and control strategy," Applied Energy, Elsevier, vol. 204(C), pages 221-241.
    7. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    8. Rathod, Dhruvang & Xu, Bin & Filipi, Zoran & Hoffman, Mark, 2019. "An experimentally validated, energy focused, optimal control strategy for an Organic Rankine Cycle waste heat recovery system," Applied Energy, Elsevier, vol. 256(C).
    9. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
    10. Ma, Xiaonan & Shu, Gequn & Tian, Hua & Xu, Wen & Chen, Tianyu, 2019. "Performance assessment of engine exhaust-based segmented thermoelectric generators by length ratio optimization," Applied Energy, Elsevier, vol. 248(C), pages 614-625.
    11. Xu, Bin & Rathod, Dhruvang & Yebi, Adamu & Filipi, Zoran & Onori, Simona & Hoffman, Mark, 2019. "A comprehensive review of organic rankine cycle waste heat recovery systems in heavy-duty diesel engine applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 145-170.
    12. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    13. Zhang, H.G. & Wang, E.H. & Fan, B.Y., 2013. "A performance analysis of a novel system of a dual loop bottoming organic Rankine cycle (ORC) with a light-duty diesel engine," Applied Energy, Elsevier, vol. 102(C), pages 1504-1513.
    14. Abedin, M.J. & Masjuki, H.H. & Kalam, M.A. & Sanjid, A. & Rahman, S.M. Ashrafur & Masum, B.M., 2013. "Energy balance of internal combustion engines using alternative fuels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 20-33.
    15. Han, Xuefeng & He, Hongwen & Wu, Jingda & Peng, Jiankun & Li, Yuecheng, 2019. "Energy management based on reinforcement learning with double deep Q-learning for a hybrid electric tracked vehicle," Applied Energy, Elsevier, vol. 254(C).
    16. Wu, Xialai & Chen, Junghui & Xie, Lei, 2019. "Fast economic nonlinear model predictive control strategy of Organic Rankine Cycle for waste heat recovery: Simulation-based studies," Energy, Elsevier, vol. 180(C), pages 520-534.
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