A comprehensive pyrolysis study of sorghum and reed stalk: TG/IR/MS analysis and machine learning-assisted kinetic prediction

Abstract

This work focused on investigating the pyrolysis characteristics of sorghum stalk (SS) and reed stalk (RS), whose potentiality as value-added feedstocks for thermochemical conversion is largely unexplored. The experimental method, thermogravimetric-infrared-mass spectrometry, was applied to reveal the thermal decomposition process, evaluate the kinetic and thermodynamic parameters, and identify the gaseous products, which could provide fundamental data and valuable information for bioenergy utilization and optimization of reactors. The average activation energy values of SS and RS by isoconversional methods are estimated to be 185.0–210.4 kJ/mol and 192.6–197.0 kJ/mol. Though kinetic parameters, especially activation energy values, play a crucial role in understanding reaction mechanism and guiding further applications, conventional methods are time-consuming and labor-intensive. In the current work, a gradient boosting regression tree model was successfully constructed to achieve fast prediction of activation energy values with a competitive accuracy of R2 > 0.85 without repetitive experiments. In addition, the established model has showed good generalization ability in new datasets, which reveals the great potential of the machine learning approach in the pyrolysis kinetic field.