IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v237y2024ipas096014812401718x.html
   My bibliography  Save this article

Machine learning model with a novel self–adjustment method: A powerful tool for predicting biomass ash fusibility and enhancing its potential applications

Author

Listed:
  • Mu, Lin
  • Wang, Zhen
  • Sun, Meng
  • Shang, Yan
  • Pu, Hang
  • Dong, Ming

Abstract

Biomass ash has been extensively studied for its potential applications, owing to its high content of alkali and alkaline earth metallic species (AAEMs). These AAEMs can act as catalysts in biomass thermochemical conversion and other industrial processes. However, AAEMs can also cause slagging and agglomeration, which can significantly impact system operations. To better understand these effects, we investigated the relationship between ash melting behavior and the chemical composition of biomass ash using a machine learning (ML) model. To enhance the model's performance, we employed a self-adjustment (SA) method, which significantly improved predictive accuracy. The SA-ETR model achieved an R2 value greater than 0.93, based on a dataset of 268 data points. We provided a detailed explanation of the SA-optimized ML model using Python's Shapley Additive Explanations (SHAP) library, which included global and local feature importance analysis, investigation of simultaneous effects between two features, and individual data point prediction analysis. The contents of K2O, SiO2, CaO, and Al2O3 were considered as the most significant factors affecting biomass ash's initial deformation temperature (IDT). The insights gained from this study can help investors and researchers reduce experimental complexity and improve system operation.

Suggested Citation

  • Mu, Lin & Wang, Zhen & Sun, Meng & Shang, Yan & Pu, Hang & Dong, Ming, 2024. "Machine learning model with a novel self–adjustment method: A powerful tool for predicting biomass ash fusibility and enhancing its potential applications," Renewable Energy, Elsevier, vol. 237(PA).
  • Handle: RePEc:eee:renene:v:237:y:2024:i:pa:s096014812401718x
    DOI: 10.1016/j.renene.2024.121650
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014812401718X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2024.121650?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:237:y:2024:i:pa:s096014812401718x. 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.