IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v239y2025ics0960148124021827.html
   My bibliography  Save this article

Molecular descriptor-based artificial intelligence frameworks for the estimation of bio-oil yield from agricultural waste

Author

Listed:
  • Yeole, Shreya P.
  • Prasad, Tanishq
  • Kundu, Debashis

Abstract

The study highlights the potential of using molecular descriptors and artificial intelligence (AI) frameworks to enhance the predictive accuracy of bio-oil yields, emphasizing the need for AI formulations in handling complex and non-linear data scenarios. An exhaustive dataset of bio-oil yield derived from various feedstocks, including wheat straw, coconut shell, rice husk, bagasse, and sawdust, is collected for the training validation and testing of AI models. Altogether, 2926 molecular descriptors with a combination of two-dimensional and three-dimensional parameters are considered for estimating bio-oil yield using Artificial Neural Network (ANN) and ANN integrated with Grey Wolf Optimization (ANN-GWO) models. Parameters for ANN and ANN-GWO are optimized using a grid approach to demonstrate the correlation coefficient of 0.959 and 0.929, respectively. Among the feedstocks, rice husk and saw dust show remarkable accuracy in the bio-oil estimation, while others perform poorly.

Suggested Citation

  • Yeole, Shreya P. & Prasad, Tanishq & Kundu, Debashis, 2025. "Molecular descriptor-based artificial intelligence frameworks for the estimation of bio-oil yield from agricultural waste," Renewable Energy, Elsevier, vol. 239(C).
    • Handle: RePEc:eee:renene:v:239:y:2025:i:c:s0960148124021827
    DOI: 10.1016/j.renene.2024.122114
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124021827
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.122114?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:239:y:2025:i:c:s0960148124021827. 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.