IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v349y2023ics0306261923010255.html
   My bibliography  Save this article

An evolutionary programming technique for evaluating the effect of ambient conditions on the power output of open cycle gas turbine plants - A case study of Afam GT13E2 gas turbine

Author

Listed:
  • Osegi, Emmanuel N.
  • Jagun, Zaid O.O.
  • Chujor, Cornelius C.
  • Anireh, Vincent I.E.
  • Wokoma, Biobele A.
  • Ojuka, Otonye

Abstract

In this research a novel Evolutionary Programming (EP) approach combined with a Continual Learning Neural Network (EP-CLNN) technique is proposed for the evaluation of Open Cycle Gas Turbines (OCGTs) net power output considering ambient temperature conditions. Studies were performed comparatively considering the proposed EP-CLNN approach, state-of-the-art linear regressors of several Polynomial (Poly) orders (Poly-1, Poly-2, Poly-3, and Poly-4) and three relatively small-to-big datasets. These approaches were applied to two open source datasets reported in the literature and a case study small dataset acquired from the Afam gas power station plant (GT132E2). The results showed that the reported Root-Mean Squared Error (RMSE) and mean net power estimates for the EP-CLNN are indeed promising considering the constraint of sequential continual learning prediction requirement, reduced variable (single input variable), simpler function set, and relatively small datasets. For the case of the big open source dataset (Dataset-1), the proposed EP-CLNN approach gave the best solution with least RMSE of 4.9926 MW over the linear regressors - Poly-1, Poly-2, Poly-3, and Poly-4 with RMSE estimates of 5.4609 MW, 5.2305 MW, 5.0986 MW, and 5.0980 MW respectively. For the case of the small dataset (Dataset-2), the estimated mean net powers were 32,974.0000 MW, 59140.0000 MW, and 96.2143 MW for Poly-1, Poly-2, and the EP techniques respectively; when compared to the base (reference) net mean power of 97.52 MW, the EP technique is the better one. Furthermore, for dataset 3, the EP technique gave an RMSE of 0.3381 MW while the combined solution of EP-CLNN gave a Mean Absolute Percentage Error (MAPE) consensus estimate of 0.1377 from a synthesized dataset of 20 evolved symbolic expressions. Thus, the EP-CLNN represents a promising approach to real-time gas turbine power output modeling with better accuracy and unique consensus expression capability.

Suggested Citation

  • Osegi, Emmanuel N. & Jagun, Zaid O.O. & Chujor, Cornelius C. & Anireh, Vincent I.E. & Wokoma, Biobele A. & Ojuka, Otonye, 2023. "An evolutionary programming technique for evaluating the effect of ambient conditions on the power output of open cycle gas turbine plants - A case study of Afam GT13E2 gas turbine," Applied Energy, Elsevier, vol. 349(C).
    • Handle: RePEc:eee:appene:v:349:y:2023:i:c:s0306261923010255
    DOI: 10.1016/j.apenergy.2023.121661
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923010255
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121661?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. Oko, C.O.C. & Njoku, I.H., 2017. "Performance analysis of an integrated gas-, steam- and organic fluid-cycle thermal power plant," Energy, Elsevier, vol. 122(C), pages 431-443.
    2. Boonnasa, S. & Namprakai, P. & Muangnapoh, T., 2006. "Performance improvement of the combined cycle power plant by intake air cooling using an absorption chiller," Energy, Elsevier, vol. 31(12), pages 2036-2046.
    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. Matjanov, Erkinjon, 2020. "Gas turbine efficiency enhancement using absorption chiller. Case study for Tashkent CHP," Energy, Elsevier, vol. 192(C).
    2. Mohapatra, Alok Ku & Sanjay,, 2014. "Thermodynamic assessment of impact of inlet air cooling techniques on gas turbine and combined cycle performance," Energy, Elsevier, vol. 68(C), pages 191-203.
    3. Oko, C.O.C. & Njoku, I.H., 2017. "Performance analysis of an integrated gas-, steam- and organic fluid-cycle thermal power plant," Energy, Elsevier, vol. 122(C), pages 431-443.
    4. Srinivas, T., 2009. "Study of a deaerator location in triple-pressure reheat combined power cycle," Energy, Elsevier, vol. 34(9), pages 1364-1371.
    5. Natarianto Indrawan & Betty Simkins & Ajay Kumar & Raymond L. Huhnke, 2020. "Economics of Distributed Power Generation via Gasification of Biomass and Municipal Solid Waste," Energies, MDPI, vol. 13(14), pages 1-18, July.
    6. Ge, Y.T. & Tassou, S.A. & Chaer, I. & Suguartha, N., 2009. "Performance evaluation of a tri-generation system with simulation and experiment," Applied Energy, Elsevier, vol. 86(11), pages 2317-2326, November.
    7. Qin, Shiyue & Chang, Shiyan, 2017. "Modeling, thermodynamic and techno-economic analysis of coke production process with waste heat recovery," Energy, Elsevier, vol. 141(C), pages 435-450.
    8. Diemuodeke, Ogheneruona E. & Mulugetta, Yacob & Imran, Muhammad, 2021. "Techno-economic and environmental feasibility analysis of rice husks fired energy system for application in a cluster of rice mills," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    9. Ladislao Eduardo Méndez-Cruz & Miguel Ángel Gutiérrez-Limón & Helen Lugo-Méndez & Raúl Lugo-Leyte & Teresa Lopez-Arenas & Mauricio Sales-Cruz, 2022. "Comparative Thermodynamic Analysis of the Performance of an Organic Rankine Cycle Using Different Working Fluids," Energies, MDPI, vol. 15(7), pages 1-23, April.
    10. Farzaneh-Gord, Mahmood & Deymi-Dashtebayaz, Mahdi, 2011. "Effect of various inlet air cooling methods on gas turbine performance," Energy, Elsevier, vol. 36(2), pages 1196-1205.
    11. Nondy, J. & Gogoi, T.K., 2022. "Tri-objective optimization of two recuperative gas turbine-based CCHP systems and 4E analyses at optimal conditions," Applied Energy, Elsevier, vol. 323(C).
    12. Ehyaei, M.A. & Mozafari, A. & Alibiglou, M.H., 2011. "Exergy, economic & environmental (3E) analysis of inlet fogging for gas turbine power plant," Energy, Elsevier, vol. 36(12), pages 6851-6861.
    13. Behnam Roshanzadeh & Ashkan Asadi & Gowtham Mohan, 2023. "Technical and Economic Feasibility Analysis of Solar Inlet Air Cooling Systems for Combined Cycle Power Plants," Energies, MDPI, vol. 16(14), pages 1-23, July.
    14. Chen, Yu-Zhi & Li, Yi-Guang & Newby, Mike A., 2019. "Performance simulation of a parallel dual-pressure once-through steam generator," Energy, Elsevier, vol. 173(C), pages 16-27.
    15. Owebor, K. & Diemuodeke, E.O. & Briggs, T.A. & Imran, M., 2021. "Power Situation and renewable energy potentials in Nigeria – A case for integrated multi-generation technology," Renewable Energy, Elsevier, vol. 177(C), pages 773-796.
    16. Mohammad Reza Majdi Yazdi & Mehdi Aliehyaei & Marc A. Rosen, 2015. "Exergy, Economic and Environmental Analyses of Gas Turbine Inlet Air Cooling with a Heat Pump Using a Novel System Configuration," Sustainability, MDPI, vol. 7(10), pages 1-28, October.
    17. Ge, Y.T. & Tassou, S.A. & Suamir, I.N., 2013. "Prediction and analysis of the seasonal performance of tri-generation and CO2 refrigeration systems in supermarkets," Applied Energy, Elsevier, vol. 112(C), pages 898-906.
    18. Wu, Tao & Ge, Zhihua & Yang, Lijun & Du, Xiaoze, 2019. "Modeling the performance of the indirect dry cooling system in a thermal power generating unit under variable ambient conditions," Energy, Elsevier, vol. 169(C), pages 625-636.
    19. Pourhedayat, Samira & Hu, Eric & Chen, Lei, 2022. "Simulation of innovative hybridizing M-cycle cooler and absorption-refrigeration for pre-cooling of gas turbine intake air: Including a case study for Siemens SGT-750 gas turbine," Energy, Elsevier, vol. 247(C).

    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:appene:v:349:y:2023:i:c:s0306261923010255. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.