Prediction of biomass gross calorific values using visible and near infrared spectroscopy

Abstract

Abstract Spectroscopy is a non-destructive sensing technology which has the potential to be an accurate method to optimize biomass-to-energy conversion processes. The objective of this study was to determine the accuracy of visible (Vis) and near infrared (NIR) spectroscopy in conjunction with chemometrics to predict gross calorific values of dedicated bioenergy crops, namely, Miscanthus and two short rotational coppice willows (SRCW). Stem samples were milled to less than 3 mm (number of samples, 44) and hyperspectral line scans were obtained using two hyperspectral systems one operated within the wavelength range 400–1000 nm and the second within the wavelength 880–1680 nm. Average reflectance spectra were obtained for a 1225 mm 2 region of interest. Partial least squares (PLS) regression models predicted gross calorific values of Miscanthus and SRCW samples over a range of 13.1–18.6 MJ kg −1 , with a root mean square error of cross-validation (RMSECV) of 0.30 MJ kg −1 and correlation of determination ( R 2 ) of 0.97. PLS prediction models for gross calorific values were also developed using only Miscanthus samples over the range 13.1–18.3 MJ kg −1 (RMSECV, 0.28 MJ kg −1 ; R 2 , 0.96). The results of this study indicated that Vis and NIR spectroscopy has the potential to rapidly characterize biomass according to its gross calorific value. This would facilitate the optimization of biomass conversion technologies.