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

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 biomass considering effective parameters ultimate analysis (such as carbon, hydrogen, oxygen, nitrogen, sulfur content) process heating rate. Through total 149 pyrolysis kinetics, this study developed an artificial model subjected it to training testing phases. proposed was validated using error analysis, sensitivity, regression, outlier detection. coefficient determination (R2) mean relative (%MRE) were calculated be 0.993 4.354%, respectively, suggesting good performance in estimation kinetic parameters. Also, sensitivity results indicated rate have strongest effect on output with relevancy factor 0.43. Eventually, showed superior compared earlier frameworks.