Quick assessment of the thermal decomposition behavior of lignocellulosic biomass by near infrared spectroscopy and its statistical analysis

Authors

  • Seung-Hwan Lee,

    Corresponding author
    1. Biomass Technology Research Center, National Institute of Advanced Industrial Science and Technology, Suehiro 2-2-2, Hiro, Kure, Hiroshima, Japan
    2. Tennessee Forest Products Center, University of Tennessee, 2506 Jacob Drive, Knoxville, Tennessee 37996-4570
    • Biomass Technology Research Center, National Institute of Advanced Industrial Science and Technology, Kure, Hiroshima, Japan
    Search for more papers by this author
  • Hyun-Woo Cho,

    1. Department of Industrial and Information Engineering, University of Tennessee, East Stadium Hall, Knoxville, Tennessee 37996
    Search for more papers by this author
  • Nicole Labbé,

    1. Tennessee Forest Products Center, University of Tennessee, 2506 Jacob Drive, Knoxville, Tennessee 37996-4570
    Search for more papers by this author
  • Myong K. Jeong

    1. Department of Industrial and Systems Engineering & RUTCOR, Rutgers, the State University of New Jersey, 640 Bartholomew Road, Piscataway, New Jersey 08854
    Search for more papers by this author

  • Seung-Hwan Lee shares the authorship with Hyun-Woo Cho.

Abstract

The application of near infrared (NIR) spectroscopy for the prediction of the thermal decomposition behavior of lignocellulosic biomass (three types of woody biomass and three types of herbaceous biomass) was successfully performed along with statistical analysis. The thermal degradation behaviors of the woody and herbaceous biomass were different because of their different chemical compositions. Herbaceous biomass was degraded at lower temperature than woody biomass. The weight-loss profiles as a function of temperature were obtained by thermogravimetric analysis (TGA) at a heating rate of 25°C/min under nitrogen gas. The weight-loss percentage at 10 temperatures in the range 150–600°C was predicted by a wavelet partial least squares (PLS) model, which showed significantly better predictive performance than the ordinary PLS model. The results show that the data predicted by the wavelet PLS model was well fitted to the original data by TGA, in which the root-mean-square error in prediction values less than 5.5 suggested that NIR spectroscopy was applicable for rapid analysis to characterize the thermal decomposition behavior of lignocellulosic biomass for energy production. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009

Ancillary