Supervised Machine Learning With Tree Ensembles and Artificial Neural Networks for Kinetic Modeling and Optimization of Biomass Pyrolysis

Accurate estimation of activation energy (Ea) in biomass pyrolysis is central to process optimization, reactor design, and bioenergy scale‐up. Conventional thermogravimetric analysis (TGA) approaches are resource‐intensive and inadequate for capturing the nonlinear compositional dependencies governing pyrolysis kinetics across diverse feedstocks. In this study, 194 experimental TGA‐derived Ea data points spanning a broad range of lignocellulosic biomass compositions were used to train supervised machine learning models using random forest (RF), eXtreme gradient boosting (XGBoost), and artificial neural network (ANN) algorithms, with cellulose, hemicellulose, and lignin content as sole inputs. XGBoost achieved the highest predictive accuracy (train R 2 0.895, test R 2 0.834, and root mean squared error [RMSE] 8.77 kJ/mol), followed by RF (train R 2 0.882, test R 2 0.819, and RMSE 9.38 kJ/mol) and ANN (train R 2 0.827, test R 2 0.666, and RMSE 11.78 kJ/mol). By eliminating the need for process variables such as temperature, heating rate, and residence time, this framework enables rapid Ea screening of novel feedstocks without full pyrolysis runs, supporting process optimization and bioenergy scale‐up.