Statistical prediction of biomethane potentials based on the composition of lignocellulosic biomass

Bioresource Technology, Volume 154, 2014, Pages 80-86.

Sune Tjalfe Thomsena, Henrik Spliidb, Hanne Østergårda, .

 a Center for BioProcess Engineering, Department of Chemical and Biochemical Engineering, Technical University of Denmark DTU, DK-2800 Kgs. Lyngby, Denmark and

b Section of Statistics and Data Analysis, Department of Applied Mathematics and Computer Science, Technical University of Denmark DTU, DK-2800 Kgs. Lyngby, Denmark.

 

Abstract

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 xCxH 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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Statistical prediction of biomethane potentials based on the composition of lignocellulosic biomass .Renewable Energy Global Innovations

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