Skip to main content

Advertisement

SpringerLink
Log in

Maximization of the power production in LNG cold energy recovery plant via genetic algorithm

  • Separation Technology, Thermodynamics
  • Published:
Korean Journal of Chemical Engineering Aims and scope Submit manuscript

Abstract

This paper presents an optimization model via genetic algorithm (GA) to maximize the power generation potential of a liquefied natural gas (LNG) cold energy recovery plant. LNG releases a large amount of cold energy during vaporization prior to transport for service, and this cold energy can be effectively utilized to generate power using a heat engine. We performed a thermodynamic analysis for a power generation system combining the organic rankine cycle (ORC) driven by LNG exergy and the direct expansion cycle. Both LNG and the working fluid in the combined ORC are light hydrocarbon mixtures, and their physical properties were estimated using the Peng-Robinson equation. We conducted a thorough investigation of the effects that the working fluid composition brought about on the thermal efficiency of the heat engine through an analysis using Aspen HYSYS interfaced with a GA-based Matlab solver. The results showed that optimization of the working fluid composition led to an increase of 58.4% in the performance of the combined ORC in terms of the net work.

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

Similar content being viewed by others

References

  1. http://www.igemfeds.org/files/yppc/2006%20Gordon%20NG.PDE

  2. J.-D. Kim, The technology trend of power generation plant utilizing LNG cold energy, KIST, Seoul (2003).

    Google Scholar 

  3. http://www.eng.usfedu/∼hchen4/Organic%20Rankine%20Cycle.htm.

  4. J. Nouman, Comparative studies and analyses of working fluids for Organic Rankine cycle, Master of Science Thesis, KTH School of Industrial Engineering and Management, Stockholm (2012).

  5. G. Shu, Y. Gao, H. Tian, H. Wei and H. Liang, Energy, 74, 428 (2014).

    Article  CAS  Google Scholar 

  6. P. Garg, P. Kumar, K. Srinivasan and P. Dutta, Appl. Therm. Eng., 51, 292 (2013).

    Article  CAS  Google Scholar 

  7. J. G. Andreasen, U. Larsen, T. Knudsen, L. Pierobon and F. Haglind, Energy, 73, 204 (2014).

    Article  CAS  Google Scholar 

  8. M. Chys, V. D. Broek, B. Vanslambrouck and D. Paepe, Energy, 44, 623 (2012).

    Article  CAS  Google Scholar 

  9. S. Lecompte, B. Ameel, D. Ziviani, V. D. Broek and D. Paepe, Energy Convers. Manage., 85, 727 (2014).

    Article  CAS  Google Scholar 

  10. L. Zhao and J. Bao, Energy Convers. Manage., 83, 203 (2014).

    Article  Google Scholar 

  11. Y. Feng, T. C. Hung, K. Greg, Y. Zhang, B. Li and J. Yang, Energy Convers. Manage., 106, 859 (2015).

    Article  CAS  Google Scholar 

  12. I. H. Choi, S. I. Lee, Y. T. Seo and D. J. Chang, Energy, 61, 179 (2013).

    Article  Google Scholar 

  13. K. H. Kim, J. M. Ha and K. C. Kim, Trans. Korean Hydrogen New Energy Soc., 25, 200 (2014).

    Article  Google Scholar 

  14. Y. Liu and K. Guo, Energy, 36, 2828 (2011).

    Article  CAS  Google Scholar 

  15. H. Sun, H. Zhu, F. Liu and H. Ding, Energy, 70, 314 (2014).

    Article  Google Scholar 

  16. A. Alabdulkarem, A. Mortazavi, Y. Hwang, R. Radermacher and P. Rogers, Appl. Therm. Eng., 31, 109 (2011).

    Article  Google Scholar 

  17. A. Baghernejad and M. Yaghoubi, Energy Convers. Manage., 52, 2193 (2011).

    Article  Google Scholar 

  18. M. Ghazi, P. Ahmadi, A. F. Sotoodeh and A. Taherkhani, Energy Convers. Manage., 58, 149 (2012).

    Article  Google Scholar 

  19. B. E. García-Flores, J. Águila-Hernández, F. García-Sánchez and R. P. Stateva, In handbook of liquefied natural gas, S. Mokhatab, J. Y. Mak, J. V. Valappil and D. A. Wood Eds., Gulf Professional Publishing, Elsevier, New York (2014).

  20. D. Y. Peng and D. B. Robinson, Ind. Eng. Chem. Fundam., 15, 59 (1976).

    Article  CAS  Google Scholar 

  21. S. A. R. Tabaei, Comparison of cubic equations of state in predicting behavior of mixtures of varying complexity, New Mexico Institute of Mining and Technology, Master’s Dissertation (1988).

  22. M. Kariznovi, H. Nourozieh and J. Abedi, Chem. Eng. Data, 57, 2535 (2012).

    Article  CAS  Google Scholar 

  23. http://www.e-education.psu.edu/png520/m11_p2.html.

  24. E. B. Cho, M. Jeong, I. J. Hwang and C. H. Kang, Trans. Korean Soc. Mech. Eng. C, 3, 55 (2015).

    Google Scholar 

  25. https://en.wikipedia.org/wiki/Natural_selection.

  26. F. J. Varela and P. Bourgine, Toward a practice of autonomous systems, The MIT Press, London (1994).

    Google Scholar 

  27. https://en.wikipedia.org/wiki/Genetic_algorithm.

  28. R. Malhotra, N. Singh and Y. Singh, Comput. Information Sci., 4, 2 (2011).

    Google Scholar 

  29. http://kr.mathworks.com/help/gads/how-the-genetic-algorithm-works.

  30. https://www.alfalaval.com/globalassets/documents/microsites/increase-efficiency/compact-heat-exchangers-improving-heat-recovery-ppi00324en.pdf.

Download references

Acknowledgements

This work was supported by a grant (19IFIP-B089065-05) from the Industrial Facilities & Infrastructure Research Program (IFIP) funded by Ministry of Land, Infrastructure and Transport of the Korean government.

Author information

Authors and Affiliations

  1. Department of Chemical Engineering, Hanyeong University, Yeosu-si, Jeonnam, 59720, Korea

    Moon Jeong

  2. School of Chemical Engineering, Chonnam National University, Gwangju, 61186, Korea

    Eun-Bi Cho & Choon-Hyoung Kang

  3. Department of Chemical and Biomolecular Engineering, Chonnam National University, Yeosu-si, Jeonnam, 59626, Korea

    Hun-Soo Byun

Authors
  1. Moon Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eun-Bi Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hun-Soo Byun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Choon-Hyoung Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Choon-Hyoung Kang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jeong, M., Cho, EB., Byun, HS. et al. Maximization of the power production in LNG cold energy recovery plant via genetic algorithm. Korean J. Chem. Eng. 38, 380–385 (2021). https://doi.org/10.1007/s11814-020-0662-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11814-020-0662-7

Keywords

Search

Navigation