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Machine learning prediction of the yield and oxygen content of bio-oil via biomass characteristics and pyrolysis conditions

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  • Yang, Ke
  • Wu, Kai
  • Zhang, Huiyan

Abstract

The bio-oil produced from biomass pyrolysis offers an important potential alternative to fossil fuels, but the yield and composition of pyrolysis product are impacted by many conditions. This work aims to predict the yield and oxygen content of bio-oil via machine learning tools based on biomass characteristics and pyrolysis conditions. For this purpose, the Random Forest (RF) algorithm is introduced and successfully applied. The performances of trained prediction models are assessed based on the regression coefficient (R2) for the test data. The results shows that the Proximate-Yield model (R2 = 0.925) has the best performance for predicting bio-oil yield, and the Ultimate-O model (R2 = 0.895) has the best performance for predicting the oxygen content of bio-oil. According to feature importance analysis, the heating rate occupied the biggest importance for predicting bio-oil yield, and the internal information of biomass is more important than that of pyrolysis conditions for predicting the bio-oil oxygen content. Besides, the modes of each variable affecting the bio-oil yield and oxygen content are described by partial dependence analysis. This work will provide a new insight for controlling the yield and oxygen content of bio-oil, which is helpful to facilitate the process optimization in engineering application.

Suggested Citation

  • Yang, Ke & Wu, Kai & Zhang, Huiyan, 2022. "Machine learning prediction of the yield and oxygen content of bio-oil via biomass characteristics and pyrolysis conditions," Energy, Elsevier, vol. 254(PB).
    • Handle: RePEc:eee:energy:v:254:y:2022:i:pb:s0360544222012233
    DOI: 10.1016/j.energy.2022.124320
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    Cited by:

    1. Zhao, Chenxi & Lu, Xueying & Jiang, Zihao & Ma, Huan & Chen, Juhui & Liu, Xiaogang, 2024. "Prediction of bio-oil yield by machine learning model based on 'enhanced data' training," Renewable Energy, Elsevier, vol. 225(C).
    2. Dong, Lu & Liu, Yuhao & Wen, Huaizhou & Zou, Chan & Dai, Qiqi & Zhang, Haojie & Xu, Lejin & Hu, Hongyun & Yao, Hong, 2023. "The deoxygenation mechanism of biomass thermal conversion with molten salts: Experimental and theoretical analysis," Renewable Energy, Elsevier, vol. 219(P1).
    3. Wang, Zhengxin & Peng, Xinggan & Xia, Ao & Shah, Akeel A. & Yan, Huchao & Huang, Yun & Zhu, Xianqing & Zhu, Xun & Liao, Qiang, 2023. "Comparison of machine learning methods for predicting the methane production from anaerobic digestion of lignocellulosic biomass," Energy, Elsevier, vol. 263(PD).
    4. Rahimi, Mohammad & Mashhadimoslem, Hossein & Vo Thanh, Hung & Ranjbar, Benyamin & Safarzadeh Khosrowshahi, Mobin & Rohani, Abbas & Elkamel, Ali, 2023. "Yield prediction and optimization of biomass-based products by multi-machine learning schemes: Neural, regression and function-based techniques," Energy, Elsevier, vol. 283(C).
    5. Saidi, Majid & Faraji, Mehdi, 2024. "Thermochemical conversion of neem seed biomass to sustainable hydrogen and biofuels: Experimental and theoretical evaluation," Renewable Energy, Elsevier, vol. 221(C).
    6. Md Sumon Reza & Zhanar Baktybaevna Iskakova & Shammya Afroze & Kairat Kuterbekov & Asset Kabyshev & Kenzhebatyr Zh. Bekmyrza & Marzhan M. Kubenova & Muhammad Saifullah Abu Bakar & Abul K. Azad & Hrido, 2023. "Influence of Catalyst on the Yield and Quality of Bio-Oil for the Catalytic Pyrolysis of Biomass: A Comprehensive Review," Energies, MDPI, vol. 16(14), pages 1-39, July.
    7. Wu, Kai & Yang, Ke & Zhu, Yiwen & Luo, Bingbing & Chu, Chenyang & Li, Mingfan & Zhang, Yuanjian & Zhang, Huiyan, 2023. "The co-pyrolysis interactionsof isolated lignins and cellulose by experiments and theoretical calculations," Energy, Elsevier, vol. 263(PC).

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