Abstract
In the present work, artificial neural network (ANN)-based machine learning models are developed to predict biomass pyrolysis kinetics. Data sets of thermogravimetric analysis and feedstock characterization from a diverse range of biomasses were used to build and test the networks. The composition of the raw biomass material was classified and used as input parameters of ANN models. Three models, which use ultimate analysis, proximate analysis, and three components as input parameters, were developed in this study. A total of 32 types of biomass raw materials were used, and 270 sets of kinetic data were obtained according to different pyrolysis conversion rates ranging from 0.1 to 0.9. Results show that increasing the number of neurons can improve the prediction accuracy. The optimized neuron number is 7-11. The largest relative deviation between experimental and modeling results for the three models are 20.80%, 14.06% and 12.85%, respectively, which proves that using cellulose, hemicellulose, and lignin as input parameters of the neural network model can better predict the activation energy of pyrolysis at each reaction stage. The particle swarm optimization algorithm could significantly improve the prediction accuracy of the BP-ANN model. The largest deviation for activated energy prediction decreases from 12.85% to 6.72%.