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Shuffled Complex Evolution-Based Performance Enhancement and Analysis of Cascade Liquefaction Process for Large-Scale LNG Production

Author

Listed:
  • Khaliq Majeed

    (Department of Chemical Engineering, COMSATS University Islamabad (CUI), Lahore Campus, Defense Road, Off Raiwind Road, Lahore 54000, Pakistan
    These authors contributed equally.)

  • Muhammad Abdul Qyyum

    (School of Chemical Engineering, Yeungnam University, Gyeongsan 712-749, Korea
    These authors contributed equally.)

  • Alam Nawaz

    (School of Chemical Engineering, Yeungnam University, Gyeongsan 712-749, Korea)

  • Ashfaq Ahmad

    (Department of Computer Science, COMSATS University Islamabad (CUI), Lahore Campus, Defense Road, Off Raiwind Road, Lahore 54000, Pakistan)

  • Muhammad Naqvi

    (Department of Engineering and Chemical Sciences, Karlstad University, 65188 Karlstad, Sweden)

  • Tianbiao He

    (Department of Gas Engineering, College of Pipeline and Civil Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Moonyong Lee

    (School of Chemical Engineering, Yeungnam University, Gyeongsan 712-749, Korea)

Abstract

Among all large-scale natural gas (NG) liquefaction processes, the mixed fluid cascade (MFC) process is recognized as a best-alternative option for the LNG production, mainly due its competitive performance. However, from a thermodynamic point of view, the MFC process is still far from its potential maximum energy efficiency due to non-optimal execution of design variables. Therefore, the energy efficiency enhancement of the MFC process remains an ongoing issue. The design optimization after fixing the main configuration of the process is one of the most economic, but challenging exercises during the design stages. In this study, shuffled complex evolution (SCE) is studied to find the optimal design of the MFC process corresponding to minimal energy consumption in refrigeration cycles. The MFC process is simulated using Aspen Hysys ® v10 and then coupled with the SCE approach, which is coded in MATLAB ® 2019a. The refrigerant composition and operating pressures for each cycle of the MFC process were optimized considering the approach temperature inside the LNG heat exchanger as a constraint. The resulting optimal MFC process saved 19.76% overall compression power and reduced the exergy destruction up to 28.76%. The thermodynamic efficiency (figure of merit) of the SCE-optimized process was 25% higher than that of the published base case. Furthermore, the optimization results also imply that there is a trade-off between the thermodynamic performance improvement and the computational cost (no. of iterations). In conclusion, SCE exhibited potential to improve the performance of highly nonlinear and complex processes such as LNG processes.

Suggested Citation

  • Khaliq Majeed & Muhammad Abdul Qyyum & Alam Nawaz & Ashfaq Ahmad & Muhammad Naqvi & Tianbiao He & Moonyong Lee, 2020. "Shuffled Complex Evolution-Based Performance Enhancement and Analysis of Cascade Liquefaction Process for Large-Scale LNG Production," Energies, MDPI, vol. 13(10), pages 1-20, May.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2511-:d:358836
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    Cited by:

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    3. Riaz, Amjad & Qyyum, Muhammad Abdul & Min, Seongwoong & Lee, Sanggyu & Lee, Moonyong, 2021. "Performance improvement potential of harnessing LNG regasification for hydrogen liquefaction process: Energy and exergy perspectives," Applied Energy, Elsevier, vol. 301(C).
    4. Lei Gao & Jiaxin Wang & Maxime Binama & Qian Li & Weihua Cai, 2022. "The Design and Optimization of Natural Gas Liquefaction Processes: A Review," Energies, MDPI, vol. 15(21), pages 1-56, October.
    5. Santos, Lucas F. & Costa, Caliane B.B. & Caballero, José A. & Ravagnani, Mauro A.S.S., 2023. "Multi-objective simulation–optimization via kriging surrogate models applied to natural gas liquefaction process design," Energy, Elsevier, vol. 262(PB).
    6. Hossein Moayedi & Amir Mosavi, 2021. "An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework," Energies, MDPI, vol. 14(4), pages 1-18, February.

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