Machine learning prediction of biochar physicochemical properties based on biomass characteristics and pyrolysis conditions
文献类型： 外文期刊
作者： Song, Yuanbo 1 ; Huang, Zipeng 1 ; Jin, Mengyu 1 ; Liu, Zhe 1 ; Wang, Xiaoxia 1 ; Hou, Cheng 2 ; Zhang, Xu 2 ; Shen, Zheng 1 ; Zhang, Yalei 1 ;
作者机构： 1.Tongji Univ, Inst New Rural Dev, Sch Elect & Informat Engn, Shanghai 201804, Peoples R China
2.Tongji Univ, Coll Environm Sci & Engn, Shanghai 200092, Peoples R China
3.Shanghai Acad Agr Sci, Ecoenvironm Protect Res Inst, Shanghai 201403, Peoples R China
4.Minist Agr & Rural Affairs, Key Lab Rural Toilet & Sewage Treatment Technol, Shanghai 201804, Peoples R China
关键词： Biochar; Pyrolysis; EXtreme gradient boosting model; Elemental distribution; Aromaticity
期刊名称：JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS （ 影响因子：5.8； 五年影响因子：5.7 ）
ISSN： 0165-2370
年卷期： 2024 年 181 卷
页码：
收录情况： SCI
摘要： Pyrolyzing waste biomass into functionalized biochar is aligned with the concept of the circular economy. The physicochemical properties of biochar are influenced by the type of biomass feedstock and pyrolysis parameters, necessitating significant time, energy, and resources for quantification. This study employed machine learning algorithms to predict the yield, elemental distribution, and degree of aromatization of biochar based on the physical and chemical properties, as well as the pyrolysis conditions of biomass. Support vector machines (SVM), multiple linear regression (MLR), nearest neighbor algorithm (KNN), random forest (RF), gradient boosting regression (GBR), and eXtreme Gradient Boosting (XGB) were comparatively analyzed. Among these algorithms, the XGB algorithm performed well in predicting biochar production and element distribution (R-2>0.99). Furthermore, PCC and SHAP analyses revealed a strong positive correlation between pyrolysis temperature and the degree of aromatization in biochar. Therefore, selecting the appropriate ML model can aid in predicting the physicochemical properties of biochar from diverse biomass sources without the necessity for complex and energy-intensive pyrolysis experiments.
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