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Design and experimental validation of an adaptive control law to maximize the power generation of a small-scale waste heat recovery system

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  • Hernandez, Andres
  • Desideri, Adriano
  • Gusev, Sergei
  • Ionescu, Clara M.
  • Den Broek, Martijn Van
  • Quoilin, Sylvain
  • Lemort, Vincent
  • De Keyser, Robin

Abstract

Increasing the energy efficiency of industrial processes is a challenge that involves, not only improving the methodologies for design and manufacturing, but optimizing performance during part-load operation and transient conditions. A well-adopted solution consists of developing waste heat recovery (WHR) systems based on Organic Rankine Cycle (ORC) power units. The highest efficiency for such cycle is obtained at low superheating values, corresponding to the situation where the system exhibits time-varying nonlinear dynamics, triggered by the fluctuating nature of the waste heat source. In this paper, an adaptive control law using the Model Predictive Control (MPC) framework is proposed. This work goes a step beyond most of the existing scientific works in the field of ORC power systems, since the MPC controller is implemented in a lab-scale prototype, and its performance compared against a gain-scheduled PID strategy. The experimental results show that the adaptive MPC outperforms the gain-scheduled PID based strategy, as it allows to accurately regulate the evaporating temperature, while keeping vapor condition at the inlet of the expander i.e., the superheating, in a safe operating range, thus increasing the net power generation.

Suggested Citation

  • Hernandez, Andres & Desideri, Adriano & Gusev, Sergei & Ionescu, Clara M. & Den Broek, Martijn Van & Quoilin, Sylvain & Lemort, Vincent & De Keyser, Robin, 2017. "Design and experimental validation of an adaptive control law to maximize the power generation of a small-scale waste heat recovery system," Applied Energy, Elsevier, vol. 203(C), pages 549-559.
  • Handle: RePEc:eee:appene:v:203:y:2017:i:c:p:549-559
    DOI: 10.1016/j.apenergy.2017.06.069
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    Cited by:

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    2. Li, Xiaoya & Xu, Bin & Tian, Hua & Shu, Gequn, 2021. "Towards a novel holistic design of organic Rankine cycle (ORC) systems operating under heat source fluctuations and intermittency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    3. 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).
    4. Marchionni, Matteo & Bianchi, Giuseppe & Karvountzis-Kontakiotis, Apostolos & Pesyridis, Apostolos & Tassou, Savvas A., 2018. "An appraisal of proportional integral control strategies for small scale waste heat to power conversion units based on Organic Rankine Cycles," Energy, Elsevier, vol. 163(C), pages 1062-1076.
    5. Koppauer, H. & Kemmetmüller, W. & Kugi, A., 2017. "Modeling and optimal steady-state operating points of an ORC waste heat recovery system for diesel engines," Applied Energy, Elsevier, vol. 206(C), pages 329-345.
    6. Xu, Bin & Li, Xiaoya, 2021. "A Q-learning based transient power optimization method for organic Rankine cycle waste heat recovery system in heavy duty diesel engine applications," Applied Energy, Elsevier, vol. 286(C).
    7. 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.
    8. 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).
    9. Cioccolanti, Luca & Tascioni, Roberto & Arteconi, Alessia, 2018. "Mathematical modelling of operation modes and performance evaluation of an innovative small-scale concentrated solar organic Rankine cycle plant," Applied Energy, Elsevier, vol. 221(C), pages 464-476.

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