Application of Random Forest Model Integrated with Feature Reduction for Biomass Torrefaction

Xiaorui Liu, Haiping Yang (), Jiamin Yang and Fang Liu
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Xiaorui Liu: School of Mine, China University of Mining and Technology, Xuzhou 221116, China
Haiping Yang: State Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Jiamin Yang: School of Mine, China University of Mining and Technology, Xuzhou 221116, China
Fang Liu: School of Low-Carbon Energy and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China

Sustainability, 2022, vol. 14, issue 23, 1-11

Abstract: A random forest (RF) model integrated with feature reduction was implemented to predict the properties of torrefied biomass based on feedstock and torrefaction conditions. Four features were selected for the prediction of fuel ratio (FR) and nitrogen content (Nt), and five features were selected for O/C and H/C ratios and HHV values. The results showed that the feature-reduced model had excellent prediction performance with the values of R 2 higher than 0.93 and RMSE less than 0.58 for all targets. Moreover, partial dependence analysis (PDA) was performed to quantify the impacts of selected features and torrefaction conditions on the targets. Temperature was the dominant factor for FR, O/C and H/C ratios, and HHV values, whereas Nt was determined most on the nitrogen content in the feedstock (Ni). This study provided comprehensive information for understanding biomass torrefaction.

Keywords: biomass torrefaction; machine learning; feature reduction; partial dependence analysis; random forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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