IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v38y2012i1p362-375.html
   My bibliography  Save this article

Efficiency enhancement of a gas turbine cycle using an optimized tubular recuperative heat exchanger

Author

Listed:
  • Sayyaadi, Hoseyn
  • Mehrabipour, Reza

Abstract

A simple gas turbine cycle namely as the Kraftwerk Union AG unit including a Siemens gas turbine model V93.1 with 60 MW nominal power and 26.0% thermal efficiency utilized in the Fars power plant located is considered for the efficiency enhancement. A typical tubular vertical recuperative heat exchanger is designed in order to integrate into the cycle as an air pre-heater for thermal efficiency improvement. Thermal and geometric specifications of the recuperative heat exchanger are obtained in a multi-objective optimization process. The exergetic efficiency of the gas cycle is maximized while the payback time for the capital investment of the recuperator is minimized. Combination of these objectives and decision variables with suitable engineering and physical constraints makes a set of the MINLP optimization problem. Optimization programming is performed using the NSGA-II algorithm and Pareto optimal frontiers are obtained in three cases including the minimum, average and maximum ambient air temperatures. In each case, the final optimal solution has been selected using three decision-making approaches including the fuzzy Bellman-Zadeh, LINMAP and TOPSIS methods. It has been shown that the TOPSIS and LINMAP decision-makers when applied on the Pareto frontier which is obtained at average ambient air temperature yields best results in comparison to other cases.

Suggested Citation

  • Sayyaadi, Hoseyn & Mehrabipour, Reza, 2012. "Efficiency enhancement of a gas turbine cycle using an optimized tubular recuperative heat exchanger," Energy, Elsevier, vol. 38(1), pages 362-375.
  • Handle: RePEc:eee:energy:v:38:y:2012:i:1:p:362-375
    DOI: 10.1016/j.energy.2011.11.048
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544211007791
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2011.11.048?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    2. Lazzaretto, A. & Toffolo, A., 2004. "Energy, economy and environment as objectives in multi-criterion optimization of thermal systems design," Energy, Elsevier, vol. 29(8), pages 1139-1157.
    3. Fiaschi, Daniele & Tapinassi, Libero, 2004. "Exergy analysis of the recuperative auto thermal reforming (R-ATR) and recuperative reforming (R-REF) power cycles with CO2 removal," Energy, Elsevier, vol. 29(12), pages 2003-2024.
    4. Sayyaadi, Hoseyn & Aminian, Hamid Reza, 2010. "Design and optimization of a non-TEMA type tubular recuperative heat exchanger used in a regenerative gas turbine cycle," Energy, Elsevier, vol. 35(4), pages 1647-1657.
    5. Sayyaadi, Hoseyn, 2009. "Multi-objective approach in thermoenvironomic optimization of a benchmark cogeneration system," Applied Energy, Elsevier, vol. 86(6), pages 867-879, June.
    6. Mortazavi, Amir & Somers, Christopher & Alabdulkarem, Abdullah & Hwang, Yunho & Radermacher, Reinhard, 2010. "Enhancement of APCI cycle efficiency with absorption chillers," Energy, Elsevier, vol. 35(9), pages 3877-3882.
    7. Kim, T.S. & Hwang, S.H., 2006. "Part load performance analysis of recuperated gas turbines considering engine configuration and operation strategy," Energy, Elsevier, vol. 31(2), pages 260-277.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sayyaadi, Hoseyn & Babaie, Meisam & Farmani, Mohammad Reza, 2011. "Implementing of the multi-objective particle swarm optimizer and fuzzy decision-maker in exergetic, exergoeconomic and environmental optimization of a benchmark cogeneration system," Energy, Elsevier, vol. 36(8), pages 4777-4789.
    2. Nondy, J. & Gogoi, T.K., 2021. "Performance comparison of multi-objective evolutionary algorithms for exergetic and exergoenvironomic optimization of a benchmark combined heat and power system," Energy, Elsevier, vol. 233(C).
    3. Sayyaadi, Hoseyn & Baghsheikhi, Mostafa, 2019. "Retrofit of a steam power plant using the adaptive neuro-fuzzy inference system in response to the load variation," Energy, Elsevier, vol. 175(C), pages 1164-1173.
    4. Sayyaadi, Hoseyn & Aminian, Hamid Reza, 2010. "Design and optimization of a non-TEMA type tubular recuperative heat exchanger used in a regenerative gas turbine cycle," Energy, Elsevier, vol. 35(4), pages 1647-1657.
    5. Luo, Zhongyang & Sultan, Umair & Ni, Mingjiang & Peng, Hao & Shi, Bingwei & Xiao, Gang, 2016. "Multi-objective optimization for GPU3 Stirling engine by combining multi-objective algorithms," Renewable Energy, Elsevier, vol. 94(C), pages 114-125.
    6. Shirazi, Ali & Najafi, Behzad & Aminyavari, Mehdi & Rinaldi, Fabio & Taylor, Robert A., 2014. "Thermal–economic–environmental analysis and multi-objective optimization of an ice thermal energy storage system for gas turbine cycle inlet air cooling," Energy, Elsevier, vol. 69(C), pages 212-226.
    7. Sayyaadi, Hoseyn & Baghsheikhi, Mostafa, 2018. "Developing a novel methodology based on the adaptive neuro-fuzzy interference system for the exergoeconomic optimization of energy systems," Energy, Elsevier, vol. 164(C), pages 218-235.
    8. Ligang Wang & Zhiping Yang & Shivom Sharma & Alberto Mian & Tzu-En Lin & George Tsatsaronis & François Maréchal & Yongping Yang, 2018. "A Review of Evaluation, Optimization and Synthesis of Energy Systems: Methodology and Application to Thermal Power Plants," Energies, MDPI, vol. 12(1), pages 1-53, December.
    9. Wang, Ligang & Yang, Yongping & Dong, Changqing & Morosuk, Tatiana & Tsatsaronis, George, 2014. "Multi-objective optimization of coal-fired power plants using differential evolution," Applied Energy, Elsevier, vol. 115(C), pages 254-264.
    10. Ahmadi, Mohammad H. & Hosseinzade, Hadi & Sayyaadi, Hoseyn & Mohammadi, Amir H. & Kimiaghalam, Farshad, 2013. "Application of the multi-objective optimization method for designing a powered Stirling heat engine: Design with maximized power, thermal efficiency and minimized pressure loss," Renewable Energy, Elsevier, vol. 60(C), pages 313-322.
    11. Sayyaadi, Hoseyn & Ghorbani, Ghadir, 2018. "Conceptual design and optimization of a small-scale dual power-desalination system based on the Stirling prime-mover," Applied Energy, Elsevier, vol. 223(C), pages 457-471.
    12. Lee, Sangick & Choi, Inhwan & Chang, Daejun, 2013. "Multi-objective optimization of VOC recovery and reuse in crude oil loading," Applied Energy, Elsevier, vol. 108(C), pages 439-447.
    13. Vuciterna, Rina & Thomsen, Michael & Popp, Jennie & Musliu, Arben, 2017. "Efficiency and Competitiveness of Kosovo Raspberry Producers," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252770, Southern Agricultural Economics Association.
    14. Collan, Mikael, 2008. "New Method for Real Option Valuation Using Fuzzy Numbers," Working Papers 466, IAMSR, Åbo Akademi.
    15. Wenyao Niu & Yuan Rong & Liying Yu & Lu Huang, 2022. "A Novel Hybrid Group Decision Making Approach Based on EDAS and Regret Theory under a Fermatean Cubic Fuzzy Environment," Mathematics, MDPI, vol. 10(17), pages 1-30, August.
    16. de Andres-Sanchez, Jorge, 2007. "Claim reserving with fuzzy regression and Taylor's geometric separation method," Insurance: Mathematics and Economics, Elsevier, vol. 40(1), pages 145-163, January.
    17. Mikhailov, L., 2004. "A fuzzy approach to deriving priorities from interval pairwise comparison judgements," European Journal of Operational Research, Elsevier, vol. 159(3), pages 687-704, December.
    18. Hongyi Sun & Bingqian Zhang & Wenbin Ni, 2022. "A Hybrid Model Based on SEM and Fuzzy TOPSIS for Supplier Selection," Mathematics, MDPI, vol. 10(19), pages 1-19, September.
    19. Liu, Yong-Jun & Zhang, Wei-Guo, 2015. "A multi-period fuzzy portfolio optimization model with minimum transaction lots," European Journal of Operational Research, Elsevier, vol. 242(3), pages 933-941.
    20. Sakawa, Masatoshi & Kato, Kosuke, 1998. "An interactive fuzzy satisficing method for structured multiobjective linear fractional programs with fuzzy numbers," European Journal of Operational Research, Elsevier, vol. 107(3), pages 575-589, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:38:y:2012:i:1:p:362-375. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.