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Fuzzy Nonlinear Dynamic Evaporator Model in Supercritical Organic Rankine Cycle Waste Heat Recovery Systems

Author

Listed:
  • Jahedul Islam Chowdhury

    (School of Water, Energy and Environment, Cranfield University, Bedford MK43 0AL, UK
    School of Management, Cranfield University, Bedford MK43 0AL, UK)

  • Bao Kha Nguyen

    (School of Engineering and Informatics, University of Sussex, Brighton BN1 9QT, UK)

  • David Thornhill

    (School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast BT9 5AH, UK)

  • Yukun Hu

    (School of Management, Cranfield University, Bedford MK43 0AL, UK)

  • Payam Soulatiantork

    (School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast BT9 5AH, UK)

  • Nazmiye Balta-Ozkan

    (School of Water, Energy and Environment, Cranfield University, Bedford MK43 0AL, UK)

  • Liz Varga

    (School of Management, Cranfield University, Bedford MK43 0AL, UK)

Abstract

The organic Rankine cycle (ORC)-based waste heat recovery (WHR) system operating under a supercritical condition has a higher potential of thermal efficiency and work output than a traditional subcritical cycle. However, the operation of supercritical cycles is more challenging due to the high pressure in the system and transient behavior of waste heat sources from industrial and automotive engines that affect the performance of the system and the evaporator, which is the most crucial component of the ORC. To take the transient behavior into account, the dynamic model of the evaporator using renowned finite volume (FV) technique is developed in this paper. Although the FV model can capture the transient effects accurately, the model has a limitation for real-time control applications due to its time-intensive computation. To capture the transient effects and reduce the simulation time, a novel fuzzy-based nonlinear dynamic evaporator model is also developed and presented in this paper. The results show that the fuzzy-based model was able to capture the transient effects at a data fitness of over 90%, while it has potential to complete the simulation 700 times faster than the FV model. By integrating with other subcomponent models of the system, such as pump, expander, and condenser, the predicted system output and pressure have a mean average percentage error of 3.11% and 0.001%, respectively. These results suggest that the developed fuzzy-based evaporator and the overall ORC-WHR system can be used for transient simulations and to develop control strategies for real-time applications.

Suggested Citation

  • Jahedul Islam Chowdhury & Bao Kha Nguyen & David Thornhill & Yukun Hu & Payam Soulatiantork & Nazmiye Balta-Ozkan & Liz Varga, 2018. "Fuzzy Nonlinear Dynamic Evaporator Model in Supercritical Organic Rankine Cycle Waste Heat Recovery Systems," Energies, MDPI, vol. 11(4), pages 1-24, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:901-:d:140596
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    References listed on IDEAS

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    Cited by:

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    3. Yıldız Koç & Hüseyin Yağlı & Ali Koç, 2019. "Exergy Analysis and Performance Improvement of a Subcritical/Supercritical Organic Rankine Cycle (ORC) for Exhaust Gas Waste Heat Recovery in a Biogas Fuelled Combined Heat and Power (CHP) Engine Thro," Energies, MDPI, vol. 12(4), pages 1-22, February.
    4. Chowdhury, Jahedul Islam & Hu, Yukun & Haltas, Ismail & Balta-Ozkan, Nazmiye & Matthew, George Jr. & Varga, Liz, 2018. "Reducing industrial energy demand in the UK: A review of energy efficiency technologies and energy saving potential in selected sectors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 1153-1178.
    5. Wanming Pan & Junkang Li & Guotao Zhang & Le Zhou & Ming Tu, 2022. "Multi-Objective Optimization of Organic Rankine Cycle (ORC) for Tractor Waste Heat Recovery Based on Particle Swarm Optimization," Energies, MDPI, vol. 15(18), pages 1-24, September.

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