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Interpretable machine learning assisted spectroscopy for fast characterization of biomass and waste.

  • 【作者】Liang, Rui,Chen, Chao,Sun, Tingxuan,Tao, Junyu,Hao, Xiaoling,Gu, Yude,Xu, Yaru,Yan, Beibei,Chen, Guanyi
  • 【作者单位】1School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China 2School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China 3Tianjin Key Lab of Biomass Wastes Utilization/Tianjin Engineering Research Center of Bio Gas/Oil Technology, Tianjin 300072, China 4School of Science, Tibet University, Lhasa 850012, China
  • 【年份】2023
  • 【卷号】Vol.160
  • 【页码】90-100
  • 【ISSN】0956-053X
  • 【关键词】Biomass and waste Elemental composition Feature selection Heating value Interpretable machine learning 
  • 【摘要】 [Display omitted] • This paper discussed the chemical insights behind BW fast characterization method. • A novel dimensional reduction method with physical significance was proposed. • The performance of the novel dimensional reduction method was com...
  • 【文献类型】 期刊
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