Su, Guangcan and Jiang, Peng (2024) Machine learning models for predicting biochar properties from lignocellulosic biomass torrefaction. Bioresource Technology, 399. p. 130519. ISSN 0960-8524, DOI https://doi.org/10.1016/j.biortech.2024.130519.
Full text not available from this repository.Abstract
This study developed six machine learning models to predict the biochar properties from the dry torrefaction of lignocellulosic biomass by using biomass characteristics and torrefaction conditions as input variables. After optimization, gradient boosting machines were the optimal model, with the highest coefficient of determination ranging from 0.89 to 0.94. Torrefaction conditions exhibited a higher relative contribution to the yield and higher heating value (HHV) of biochar than biomass characteristics. Temperature was the dominant contributor to the elemental and proximate composition and the yield and HHV of biochar. Feature importance and SHapley Additive exPlanations revealed the effect of each influential factor on the target variables and the interactions between these factors in torrefaction. Software that can accurately predict the element, yield, and HHV of biochar was developed. These findings provide a comprehensive understanding of the key factors and their interactions influencing the torrefaction process and biochar properties.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Torrefaction; Temperature; Gradient boosting machines; Feature importance; Predictive software |
| Subjects: | S Agriculture > S Agriculture (General) T Technology > TJ Mechanical engineering and machinery |
| Divisions: | Faculty of Engineering > Department of Mechanical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 22 Oct 2024 05:52 |
| Last Modified: | 22 Oct 2024 05:52 |
| URI: | http://eprints.um.edu.my/id/eprint/45459 |
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