Skip to main content
SpringerLink
Log in

Set point optimization of controlled Organic Rankine Cycle systems

  • Article
  • Engineering Thermophysics
  • Published:
Chinese Science Bulletin

Abstract

In this paper, an approach to optimize set points is proposed for controlled Organic Rankine Cycle (ORC) systems. Owing to both disturbances and variations of operating point existing in ORC systems, it is necessary to optimize the set points for controlled ORC systems so as to improve the energy conversion efficiency. At first, the optimal set points of controlled ORC systems are investigated by revisiting performance analysis and optimization of ORC systems. The expected set points of the evaporating pressure and the temperature at evaporator outlet are then determined by combining genetic algorithm with least squares support vector machine (GA-LSSVM). Simulation results show that the predicted results by GA-LSSVM can be regarded as the optimal set points of controlled ORC systems with varying operating conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Tchanche BF, Lambrinos G, Frangoudakis A et al (2011) Low-grade heat conversion into power using Organic Rankine Cycles—a review of various applications. Renew Sustain Energy Rev 15:3963–3979

    Article  Google Scholar 

  2. Hung TC, Shai TY, Wang SK (1997) A review of Organic Rankine Cycles (ORCs) for the recovery of low-grade waste heat. Energy 22:661–667

    Article  Google Scholar 

  3. Liu BT, Chien KH, Wang CC (2004) Effect of working fluids on Organic Rankine Cycle for waste heat recovery. Energy 29:1207–1217

    Article  Google Scholar 

  4. Mago PJ, Chamra LM, Srinivasa K et al (2008) An examination of regenerative Organic Rankine Cycles using dry fluids. Appl Therm Eng 28:998–1007

    Article  Google Scholar 

  5. Quoilin S, Declaye S, Tchanche BF et al (2011) Thermo-economic optimization of waste heat recovery Organic Rankine Cycles. Appl Therm Eng 31:2885–2893

    Article  Google Scholar 

  6. Dai YP, Wang JF, Gao L (2009) Parametric optimization and comparative study of Organic Rankine Cycle (ORC) for low grade waste heat recovery. Energy Convers Manage 50:576–582

    Article  Google Scholar 

  7. Roy JP, Misra A (2012) Parametric optimization and performance analysis of a regenerative Organic Rankine Cycle using R-123 for waste heat recovery. Energy 39:227–235

    Article  Google Scholar 

  8. Roy JP, Mishra MK, Misra A (2011) Parametric optimization and performance analysis of a regenerative Organic Rankine Cycle using low–grade waste heat for power generation. Int J Green Energy 8:173–196

    Article  Google Scholar 

  9. Roy JP, Mishra MK, Misra A (2010) Parametric optimization and performance analysis of a waste heat recovery system using Organic Rankine Cycle. Energy 35:5049–5062

    Article  Google Scholar 

  10. Hung TC (2001) Waste heat recovery of Organic Rankine Cycle using dry fluids. Energy Convers Manag 42:539–553

    Article  Google Scholar 

  11. Sun J, Li WH (2011) Operation optimization of an Organic Rankine Cycle (ORC) heat recovery power plant. Appl Therm Eng 31:2032–2041

    Article  Google Scholar 

  12. Quoilin S, Aumann R, Grill A et al (2011) Dynamic modeling and optimal control strategy of waste heat recovery Organic Rankine Cycles. Appl Energy 88:2183–2190

    Article  Google Scholar 

  13. Zhang JH, Zhou YL, Wang R et al (2014) Modeling and constrained multivariable predictive control for ORC (Organic Rankine Cycle) based waste heat energy conversion systems. Energy 66:128–138

    Article  Google Scholar 

  14. Mustaffa Z, Yusof Y, Kamaruddin SS (2013) Enhanced artificial bee colony for training least squares support vector machines in commodity price forecasting. J Comput Sci 5:196–205

    Article  Google Scholar 

  15. Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9:293–300

    Article  Google Scholar 

  16. Wu CH, Tzeng GH, Lin RH (2009) A novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert Syst Appl 36:4725–4735

    Article  Google Scholar 

  17. Yang Z, Gu XS, Liang XY et al (2010) Genetic algorithm-least squares support vector regression based predicting and optimizing model on carbon fiber composite integrated conductivity. Mater Des 31:1042–1049

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Basic Research Program of China (2011CB710706) and the National Natural Science Foundation of China (51210011, 61374025).

Author information

Authors and Affiliations

  1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China

    Jianhua Zhang

  2. School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China

    Mingming Lin, Fei Shi & Jia Meng

  3. The Beijing Key Laboratory of New and Renewable Energy, North China Electric Power University, Beijing, 102206, China

    Jinliang Xu

Authors
  1. Jianhua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingming Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fei Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jia Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jinliang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianhua Zhang.

Additional information

SPECIAL TOPIC: Deep Utilization of Boiler Low-Temperature Flue Gas

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Lin, M., Shi, F. et al. Set point optimization of controlled Organic Rankine Cycle systems. Chin. Sci. Bull. 59, 4397–4404 (2014). https://doi.org/10.1007/s11434-014-0590-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11434-014-0590-1

Keywords

Search

Navigation