Statistical prediction of biomethane potentials based on the composition of lignocellulosic biomass
2014
Thomsen, Sune Tjalfe | Spliid, Henrik | Østergård, Hanne
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 (R2>0.96), including the four biomass components cellulose (xC), hemicellulose (xH), lignin (xL) and residuals (xR=1−xC−xH−xL) 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 xC, xH and xR were not significantly different based on the dataset. The model was extended with an effect of different methods of analysing the biomass constituents content (DA) which had a significant impact. In conclusion, the best prediction of BMP is pBMP=347xC+H+R−438xL+63DA.
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