초록
Mixture models are introduced as a new and stronger methodology for statistical prediction of biomethane potentials (BPM) from lignocellulosic biomass compared to the linear regression models previously used. A large dataset from literature combined with our own data were analysed using canonical linear and quadratic mixture models. The full model to predict BMP (R<SUP>2</SUP>>0.96), including the four biomass components cellulose (x<SUB>C</SUB>), hemicellulose (x<SUB>H</SUB>), lignin (x<SUB>L</SUB>) and residuals (x<SUB>R</SUB>=1-x<SUB>C</SUB>-x<SUB>H</SUB>-x<SUB>L</SUB>) had highly significant regression coefficients. It was possible to reduce the model without substantially affecting the quality of the prediction, as the regression coefficients for x<SUB>C</SUB>, x<SUB>H</SUB> and x<SUB>R</SUB> were not significantly different based on the dataset. The model was extended with an effect of different methods of analysing the biomass constituents content (D<SUB>A</SUB>) which had a significant impact. In conclusion, the best prediction of BMP is pBMP=347x<SUB>C+H+R</SUB>-438x<SUB>L</SUB>+63D<SUB>A</SUB>.