Predictive modelling of lignocellulosic biomass fuel changes during torrefaction via mass reduction

Abstract

Biomass utilization as an alternative to fossil fuels is increasingly prioritized due to its potential to mitigate environmental pollution and enhance energy security. Torrefaction, a thermochemical process conducted under oxygen-lean conditions, improves biomass fuel quality by increasing energy density and hydrophobicity. However, optimizing this process requires a comprehensive understanding of biomass changes, particularly mass loss and its impact on elemental composition, proximate analysis, and energy indices. This study developed a predictive model that leverages enhancement ratio criterions to improve accuracy in forecasting changes during torrefaction. The proposed model exhibited superior performance compared to previous approaches, achieving an R² of 0.9725 for energy yield and 0.9339 for energy density enhancement factor. Distinct trends were observed, with a linear relationship for energy yield and logarithmic correlations for other parameters. The findings emphasize the importance of tailoring torrefaction conditions based on biomass types (e.g., herbaceous or lignocellulosic) to achieve optimal energy output. Compared to earlier models, this approach demonstrated higher precision and broader applicability by incorporating characteristics of untreated biomass. This research provides a robust framework for sustainable energy production, advancing the field of bioenergy and offering valuable insights for future studies targeting efficient biomass utilization and process optimization.