Evaluation and Prediction of Heating Value of Lignocellulosic Biomass Based on Elemental Composition

Abstract

This study evaluates and predicts the higher heating value (HHV) of lignocellulosic biomass derived from agricultural residues and synthetic mixtures with controlled lignin content. Experimental procedures comprised comprehensive elemental analysis using a CHNSO analyser and calorimetric measurements with an IKA C 6000 Isoperibol calorimeter. Results showed that increasing lignin content from 0% to 100% increased carbon content from 45.68% to 65.54% and, correspondingly, HHV from 17.76 to 24.34 MJ/kg. This study compared several empirical models for HHV prediction, revealing that the Sheng equation achieved the highest accuracy, with an average deviation of only 3.8% from measured values and a coefficient of determination (R²) of 0.94. Thermogravimetric and differential scanning calorimetry analyses confirmed greater thermal stability in lignin-rich samples. The study concludes that integrating precise elemental analysis with validated empirical models enables a reliable and efficient evaluation of the energy potential of lignocellulosic biomass, supporting the development of simplified approaches to fuel evaluation and selection for bioenergy applications.