期刊
BIORESOURCE TECHNOLOGY
卷 288, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.biortech.2019.121527
关键词
Machine learning; Pyrolysis; Lignocellulosic biomass; Biochar yield; Carbon contents in biochar
资金
- Singapore RIE2020 Advanced Manufacturing and Engineering (AME) Programmatic grant Accelerated Materials Development for Manufacturing by the Agency for Science, Technology and Research [A1898b0043]
In the study, machine learning was used to develop prediction models for yield and carbon contents of biochar (C-char) based on the pyrolysis data of lignocellulosic biomass, and explore inside information underlying the models. The results suggested that random forest could accurately predict biochar yield and C-char according to biomass characteristics and pyrolysis conditions. Furthermore, the relative contribution of pyrolysis conditions was higher than that of biomass characteristics for both yield (65%) and C-char (53%). For biomass characteristics, structural information was more important than elements compositions for accurately predicting biochar yield and it was inverse for C-char. The partial dependence plot analysis showed the impact way of each influential factor on the target variable and the interactions among these factors in the pyrolysis process. The present work provided new insights for understanding pyrolysis process of biomass and improving biochar yield and C-char.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据