IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v245y2025ics0960148125003696.html
   My bibliography  Save this article

Advanced optimization techniques for biogas-fueled cogeneration system using soft computing approaches

Author

Listed:
  • Rostamnejad Takleh, H.
  • Razbin, Milad
  • Mehdizadeh, Sevda
  • Hashemzade, Hediye
  • Feili, Milad
  • Parikhani, Towhid
  • Azariyan, Hossein

Abstract

This study addresses critical gaps in biogas-based systems by proposing a novel cogeneration framework integrating advanced thermodynamic cycles, including the Kalina cycle, organic Rankine cycle, and vapor compression refrigeration cycle. Key innovations include utilizing the Kalina cycle separator's liquid output as a heat source and replacing the conventional condenser with a thermoelectric generator to reduce exergy loss and enhance power generation. Advanced predictive modeling using response surface methodology (RSM) and artificial neural networks (ANN), coupled with a genetic algorithm, enables dual-objective optimization of energy efficiency (EE) and total cost of the plant (TCOP). The proposed system achieved significant optimization results, demonstrating a TCOP of 64.68 $/MWh and an EE of 51.97 % under optimal conditions (ACPR = 7.17, T4 = 1400 K, YB = 69.80 %, TEVA = 270.36 K) with prediction errors below 2 %. Exergy destruction analysis revealed that the combustion chamber accounted for 54.9 % (1203 kW) of total exergy destruction, followed by the heat recovery unit at 16.67 % (365.2 kW) and the air preheater at 7.57 % (166 kW). Together, these components contributed 79.14 % of total exergy destruction. Other components, such as the thermoelectric generator and air compressor, exhibited moderate destruction rates of 84.04 kW and 116.3 kW, respectively.

Suggested Citation

  • Rostamnejad Takleh, H. & Razbin, Milad & Mehdizadeh, Sevda & Hashemzade, Hediye & Feili, Milad & Parikhani, Towhid & Azariyan, Hossein, 2025. "Advanced optimization techniques for biogas-fueled cogeneration system using soft computing approaches," Renewable Energy, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:renene:v:245:y:2025:i:c:s0960148125003696
    DOI: 10.1016/j.renene.2025.122707
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125003696
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.122707?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.

    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:renene:v:245:y:2025:i:c:s0960148125003696. 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.

    We have no bibliographic references for this item. You can help adding them by using 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/renewable-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.