Article ; Online: Energy utilization of agricultural waste: Machine learning prediction and pyrolysis transformation.
Waste management (New York, N.Y.)
2024 Volume 175, Page(s) 235–244
Abstract: The rapid screening of agricultural waste materials for capacitor preparation holds significant importance in comprehending the relationship between material properties and enhancing experimental efficiency. In this study, we developed two machine ... ...
Abstract | The rapid screening of agricultural waste materials for capacitor preparation holds significant importance in comprehending the relationship between material properties and enhancing experimental efficiency. In this study, we developed two machine learning models to predict electrode material characteristics using 2997 data points extracted from 235 articles. The identification and influence of key features on prediction indices provide a theoretical foundation for subsequent practical preparation. Through regression analysis and index evaluation, corn straw emerged as the optimal material for capacitor preparation, leading us to propose a one-step activation and two-step modification approach to convert corn straw into porous biochar. By modifying biochar with Co(NO |
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MeSH term(s) | Pyrolysis ; Agriculture ; Carbon ; Machine Learning ; Zea mays ; Charcoal |
Chemical Substances | biochar ; Carbon (7440-44-0) ; Charcoal (16291-96-6) |
Language | English |
Publishing date | 2024-01-13 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 2001471-5 |
ISSN | 1879-2456 ; 0956-053X |
ISSN (online) | 1879-2456 |
ISSN | 0956-053X |
DOI | 10.1016/j.wasman.2024.01.003 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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