Predictive modelling of biomass pyrolysis: Product estimation using thermogravimetry, mass balance, and empirical correlations

The dynamic representation of pyrolysis is essential for modeling thermochemical conversion processes, such as combustion and gasification. This study proposes an innovative methodology that integrates thermogravimetric analysis (TGA) and elemental analysis of biochar to describe the evolution of solid, liquid, and gaseous products as a function of temperature. Empirical correlations for key compounds, such as CO, CO₂, CH₄, H₂, and H₂O, are combined with a multivariate optimization approach. By employing more equations than unknowns, the method minimizes errors through weighted least squares adjustments, ensuring mass balance and consistency with empirical trends. The model predicts a significant increase in non-condensable gases, including H₂ and CH₄, between 400 °C and 600 °C, attributed to reforming and volatile decomposition reactions. At approximately 400 °C, biochar achieves its highest energy quality, with a calorific value of up to 31 MJ/kg. Additionally, the model dynamically estimates the enthalpy of formation for biochar and accurately predicts the evolution of gas, liquid, and solid yields as a function of temperature. Finally, a sensitivity analysis is presented to evaluate the stoichiometric compositions based on the elemental composition of biomass and the pyrolysis temperature within typical biomass ranges on a dry ash-free (DAF) basis. The methodology facilitates the optimization and design of industrial thermochemical processes, providing a flexible and reliable tool to enhance process efficiency and understand pyrolysis dynamics under various conditions.