TY - JOUR
T1 - Statistical prediction of biomethane potentials based on the composition of lignocellulosic biomass
AU - Thomsen, Sune Tjalfe
AU - Spliid, Henrik
AU - Østergård, Hanne
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Anaerobic digestion (AD)
KW - Biogas
KW - Biomethane potential (BMP)
KW - Lignocellulose
KW - Mixture model
U2 - 10.1016/j.biortech.2013.12.029
DO - 10.1016/j.biortech.2013.12.029
M3 - Journal article
C2 - 24384313
AN - SCOPUS:84891354452
SN - 0960-8524
VL - 154
SP - 80
EP - 86
JO - Bioresource Technology
JF - Bioresource Technology
ER -