Work

Source discrimination by classical characterization methods, FTIR and statistical analysis – a prerequisite for thermochemical conversion of agriculture biomass residues by torrefaction and pyrolysis

Year: 2025

Type: article

Abstract: The possibility for rapid and facile biomass characterization and classification, including prediction, is strongly beneficial before suitable thermal conversion. Here we explored the hypothesis that classical destructive methods (proximate, ultimate and compositional analysis) and infrared spectroscopy can discriminate, classify and predict the type of biomass resources, focusing on five biological classes of agriculture residues (stalks, hulls, shells, pits, seed cakes). Proximate and ultimate analysis revealed clustering in tridimensional representation and Van Krevelen diagram. Despite showing distinct spider plot patterns for each class, compositional analysis was less useful for direct discrimination. Instead, sample classes were correctly discriminated by principal component analysis and hierarchical clustering. We proposed FTIR as a facile, rapid alternative to classical, destructive methods, which differentiates the biomass residues mainly by the vibration bands of fatty acids and carbohydrates and less by those of lignin. The fine k-nearest neighbors machine learning algorithm applied to FTIR data correctly predicted the class for 96 % of the samples, with 0.94595 validation accuracy, much better than for the destructive methods. Particular features are preserved after torrefaction and pyrolysis, classification of liquid products being the same as the biomass sources. Stepwise pyrolysis can flatten the differences in biomass sources, generating products with more uniform composition. • 21 biomass residues in 5 classes were studied for discrimination and classification. • Proximate, ultimate and compositional analysis methods were compared with FTIR. • Unsupervised PCA and hierarchical clustering provide good class discrimination. • Fine KNN model on FTIR have 0.95 accuracy in class prediction for 96 % of samples. • Classification was confirmed for the torrefaction and pyrolysis products of biomass.

Source: Energy

DOI: 10.1016/j.energy.2025.137637

Authors: Mihai Brebu, Elena Butnaru, Elena Stoleru, Siong Fong Sim

Institutions: Institutul de Chimie Macromoleculară Petru Poni, Universiti Malaysia Sarawak

Language: English

Topic: Thermochemical Biomass Conversion Processes

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Responsible consumption and production, Zero hunger

Open Access status: hybrid

Estimated APC paid: $4,260