Comprehensive characterization and predictive modeling of Spanish biomass for efficient solid biofuel utilization
Metadatos
Mostrar el registro completo del ítemAutor
Lozano, Emilio J.; Rico Castro, Nuria; Calero De Hoces, Francisca Mónica; Martín-Lara, María ÁngelesEditorial
Elsevier
Materia
Biomass characterization Fuel properties Combustion behaviour
Fecha
2025-11Referencia bibliográfica
Lozano, E. J., Rico, N., Calero, M., & Martín-Lara, M. Á. (2025). Comprehensive characterization and predictive modeling of Spanish biomass for efficient solid biofuel utilization. Thermal Science and Engineering Progress, 67(104127), 104127. https://doi.org/10.1016/j.tsep.2025.104127
Patrocinador
Universidad de Granada / CBUA (Open access)Resumen
Biomass is a promising renewable energy source in Spain, but its heterogeneous composition affects combustion
behavior and operational suitability. This study investigates the main properties, and slagging and fouling
behavior of 18 representative Spanish biomass samples, focusing on identify significant patterns and differences
between woody, fruit and herbaceous groups. Standard tests were conducted for elemental, proximate, and
energy analysis, and slagging and fouling indices were calculated to assess operational suitability. Statistical
methods were used to identify significant differences in chemical properties and higher heating values (HHV)
among the biomass groups. Results showed carbon content between 39.41 % and 55.54 %, hydrogen between
4.64 % and 12.38 %, and sulfur below 0.15 %, minimizing corrosion risks. Ash content varied widely
(0.29–22.61 %), with chestnut pellets exhibiting the lowest values and wheat straw the highest. Pistachio shell
achieved the highest higher heating value (HHV, 25.13 MJ/kg), while wheat straw had the lowest (14.96 MJ/
kg). There was significant variability in major and minor elements, with some samples showing elevated levels of
toxic metals like chromium and lead. Non-parametric tests, including both our experimental dataset and 88
additional biomass characterizations from literature, identified significant differences across different biomass
groups, with woody biomasses showing more favorable combustion properties. A robust predictive model for
HHV estimation was developed using both experimental and literature data, achieving a strong correlation with
measured values.





