Pyrolysis mechanism study on xylose by combining experiments, chemical reaction neural networks and density...

Chemical reaction neural networks (CRNN) and density functional theory (DFT) are gaining attention in biomass pyrolysis mechanism research. Reaction pathways are often speculated based on a single method, influenced by expert knowledge. To address this, the pyrolysis mechanism of xylose, a hemicellulose model compound, is studied using thermogravimetric-Fourier transform infrared spectroscopy (TG-FTIR), CRNN, and DFT. Based on the TG-FTIR measured products, seven main pyrolysis products of xylose are applied. A CRNN model of 8 species (7 + 1) and 10 reactions, including kinetic parameters, is established, achieving an MAE below 2 × 10^-2. Furthermore, the detailed reaction pathways and potential energy surfaces of evolution into each species are studied through DFT calculations. The activation energy for the xylose ring-opening, ring-condensation, and dehydration reactions, as obtained from the CRNN model, are 152.78 kJ/mol, 485.81 kJ/mol, and 320.01 kJ/mol, respectively. The deviations from the theoretical DFT calculations are less than 37 %, demonstrating good agreement. Compared to dehydration and ring condensation reactions, xylose is most susceptible to ring-opening reactions to form d-xylose. d-xylose easily undergoes hemi-acetalization, isomerization and dehydration at the 3-OH + 2-H, 5-OH + 4-H and 4-OH + 3-H sites. Xylose is most susceptible to dehydration reactions at the 1-OH + 2-H and 2-OH + 3-H sites to generate dehydrated xylose. Finally, xylose can also generate furfural through a cyclic condensation reaction. The conclusion can promote the improvement and development of hemicellulose pyrolysis kinetics models, and the research process can provide experience for the pyrolysis mechanism of other materials.