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Fast economic nonlinear model predictive control strategy of Organic Rankine Cycle for waste heat recovery: Simulation-based studies


  • Wu, Xialai
  • Chen, Junghui
  • Xie, Lei


Effective control for Organic Rankine Cycle (ORC) systems is required to ensure safety of each component and attain satisfactory performance in spite of the waste heat sources varying in a broad range. To maximally recover the waste heat and to handle the multivariate constraints during the ORC transient operation, in this paper an economic nonlinear model predictive controller (EMPC) using the net power output as an objective is designed. To fast obtain a solution of EMPC in practical applications, the computation of the gradient of the EMPC objective is simplified and the quasi-sequential method is employed for the online dynamic optimization of EMPC. Unlike the conventional nonlinear model predictive control (MPC) scheme, the results in a case study show that the proposed EMPC can quickly improve the net power output of the ORC system during the operation while still satisfying the load tracking requirements.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:energy:v:180:y:2019:i:c:p:520-534
    DOI: 10.1016/

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    References listed on IDEAS

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    Cited by:

    1. Oravec, Juraj & Horváthová, Michaela & Bakošová, Monika, 2020. "Energy efficient convex-lifting-based robust control of a heat exchanger," Energy, Elsevier, vol. 201(C).
    2. Pili, R. & Eyerer, S. & Dawo, F. & Wieland, C. & Spliethoff, H., 2020. "Development of a non-linear state estimator for advanced control of an ORC test rig for geothermal application," Renewable Energy, Elsevier, vol. 161(C), pages 676-690.
    3. 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).


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