Prediction of Pyrolysis Kinetics of Biomass: New Insights from Artificial Intelligence-Based Modeling

Abstract

The present work introduces a quantitative structure-property relationship (QSPR)-based stochastic gradient boosting (SGB) decision tree framework for simulating and capturing of the thermal decomposition kinetics of biomass considering effective parameters of the ultimate analysis (such as carbon, hydrogen, oxygen, nitrogen, and sulfur content) and process heating rate. Through a total of 149 pyrolysis kinetics, this study developed an artificial model and subjected it to training and testing phases. The proposed model was validated using error analysis, sensitivity, regression, and outlier detection. The coefficient of determination (R2) and mean relative error (%MRE) were calculated to be 0.993 and 4.354%, respectively, suggesting good performance in the estimation of the pyrolysis kinetic parameters. Also, the sensitivity results indicated the process heating rate to have the strongest effect on the model output with a relevancy factor of 0.43. Eventually, the proposed model showed superior performance compared to earlier frameworks.