Estimation of calorific value of biomass from its elementary components by regression analysis

Abstract

The calorific value is one of the most important properties of biomass fuels for design calculations or numerical simulations in thermochemical conversion systems for biomass. There are a number of formulae proposed in the literature to estimate the calorific value of biomass fuels from its elementary components by i.e. proximate, ultimate and chemical analysis composition. In this thesis, these correlations were evaluated statistically by Regression Analysis based on a larger database of biomass samples collected from the open literature. It was found that the correlations based on linear multiple regression analysis is the most accurate. The correlations based on the non-linear regression analysis have very low accuracy. The low accuracy of previous correlations is mainly due to the limitation of samples used for deriving them. To achieve a higher accuracy, new correlations were proposed to estimate the Calorific value by Regression analysis based on present database. The new correlation between the Calorific value and elemental components of biomass could be conveniently used to estimate the Calorific Value from Regression analysis. The new formula, based on the composition of main elements (in wt. %) C, H, O, N and S based on nonlinear regression analysis is
C2+ C × O2+ 0.03 C × H + 0.60 C – O + 0.11 O × N + 0.53 S – 0.33 S × O = Calorific Value (Mj/Kg)
whose R-squared value is 0.956