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

Author

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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)

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.

Suggested Citation

  • Xiaorui Liu & Haiping Yang & Jiamin Yang & Fang Liu, 2022. "Application of Random Forest Model Integrated with Feature Reduction for Biomass Torrefaction," Sustainability, MDPI, vol. 14(23), pages 1-11, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16055-:d:990351
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    References listed on IDEAS

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    1. Onsree, Thossaporn & Tippayawong, Nakorn & Phithakkitnukoon, Santi & Lauterbach, Jochen, 2022. "Interpretable machine-learning model with a collaborative game approach to predict yields and higher heating value of torrefied biomass," Energy, Elsevier, vol. 249(C).
    2. Onsree, Thossaporn & Tippayawong, Nakorn, 2021. "Machine learning application to predict yields of solid products from biomass torrefaction," Renewable Energy, Elsevier, vol. 167(C), pages 425-432.
    3. Leng, Erwei & He, Ben & Chen, Jingwei & Liao, Gaoliang & Ma, Yinjie & Zhang, Feng & Liu, Shuai & E, Jiaqiang, 2021. "Prediction of three-phase product distribution and bio-oil heating value of biomass fast pyrolysis based on machine learning," Energy, Elsevier, vol. 236(C).
    4. Li, Jie & Pan, Lanjia & Suvarna, Manu & Tong, Yen Wah & Wang, Xiaonan, 2020. "Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning," Applied Energy, Elsevier, vol. 269(C).
    5. Kartal, Furkan & Özveren, Uğur, 2022. "Prediction of torrefied biomass properties from raw biomass," Renewable Energy, Elsevier, vol. 182(C), pages 578-591.
    6. González-Arias, J. & Gómez, X. & González-Castaño, M. & Sánchez, M.E. & Rosas, J.G. & Cara-Jiménez, J., 2022. "Insights into the product quality and energy requirements for solid biofuel production: A comparison of hydrothermal carbonization, pyrolysis and torrefaction of olive tree pruning," Energy, Elsevier, vol. 238(PC).
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    2. Montree Wongsiriwittaya & Teerapat Chompookham & Bopit Bubphachot, 2023. "Improvement of Higher Heating Value and Hygroscopicity Reduction of Torrefied Rice Husk by Torrefaction and Circulating Gas in the System," Sustainability, MDPI, vol. 15(14), pages 1-13, July.
    3. Henrique Piqueiro & Reinaldo Gomes & Romão Santos & Jorge Pinho de Sousa, 2023. "Managing Disruptions in a Biomass Supply Chain: A Decision Support System Based on Simulation/Optimisation," Sustainability, MDPI, vol. 15(9), pages 1-25, May.
    4. Asya İşçen & Kerem Öznacar & K. M. Murat Tunç & M. Erdem Günay, 2023. "Exploring the Critical Factors of Biomass Pyrolysis for Sustainable Fuel Production by Machine Learning," Sustainability, MDPI, vol. 15(20), pages 1-20, October.

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