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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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    Cited by:

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    2. Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2023. "Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
    3. 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.
    4. Seraj, Somaye & Azargohar, Ramin & Dalai, Ajay K., 2025. "Dry torrefaction and hydrothermal carbonization of biomass to fuel pellets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
    5. 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.
    6. 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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