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Surrogate empowered Sim2Real transfer of deep reinforcement learning for ORC superheat control

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  • Lin, Runze
  • Luo, Yangyang
  • Wu, Xialai
  • Chen, Junghui
  • Huang, Biao
  • Su, Hongye
  • Xie, Lei

Abstract

The Organic Rankine Cycle (ORC) is widely used in industrial waste heat recovery due to its simple structure and easy maintenance. However, in the context of smart manufacturing in the process industry, traditional model-based optimization control methods are unable to adapt to the varying operating conditions of the ORC system or sudden changes in operating modes. Deep reinforcement learning (DRL) has significant advantages in situations with uncertainty as it directly achieves control objectives by interacting with the environment without requiring an explicit model of the controlled plant. Nevertheless, direct application of DRL to physical ORC systems presents unacceptable safety risks, and its generalization performance under model-plant mismatch is insufficient to support ORC control requirements. Therefore, this paper proposes a Sim2Real transfer learning-based DRL control method for ORC superheat control, which aims to provide a new simple, feasible, and user-friendly solution for energy system optimization control. Experimental results show that the proposed method greatly improves the training speed of DRL in ORC control problems and solves the generalization performance issue of the agent under multiple operating conditions through Sim2Real transfer.

Suggested Citation

  • Lin, Runze & Luo, Yangyang & Wu, Xialai & Chen, Junghui & Huang, Biao & Su, Hongye & Xie, Lei, 2024. "Surrogate empowered Sim2Real transfer of deep reinforcement learning for ORC superheat control," Applied Energy, Elsevier, vol. 356(C).
    • Handle: RePEc:eee:appene:v:356:y:2024:i:c:s0306261923016744
    DOI: 10.1016/j.apenergy.2023.122310
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    References listed on IDEAS

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    1. Zhang, Jianhua & Zhou, Yeli & Wang, Rui & Xu, Jinliang & Fang, Fang, 2014. "Modeling and constrained multivariable predictive control for ORC (Organic Rankine Cycle) based waste heat energy conversion systems," Energy, Elsevier, vol. 66(C), pages 128-138.
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    4. 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).
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