Machine learning based prediction of biomass pyrolysis with detailed reaction kinetics for thermally-thick particles: from 1D to 0D

Abstract

In reactor-scale CFD modeling of biomass pyrolysis with thermally-thick particles, zero-dimensional (0D) models coupled with lumped kinetics are commonly used, as they are simple and computationally efficient. However, intra-particle heat transfer, which cannot be directly implemented in 0D models, has significant effects on pyrolysis behaviors of thermally-thick biomass particles. Additionality, lumped kinetics usually fails to predict detailed composition of pyrolysis products. To overcome these issues, a widely-used one-dimensional (1D) model that can directly incorporate intra-particle heat transfer was employed with a detailed pyrolysis kinetics in this work to develop a corrected 0D (Cor-0D) model for accurate CFD modeling of biomass pyrolysis inside thermally-thick particles. Correction coefficients of external heat transfer, particle diameter, and pyrolysis reactions were introduced by comparing predictions of the 1D model with those of the 0D model quantitatively to reflect the effects of respective factors. The comparison demonstrates that if correction coefficients are properly determined, predictions of the developed Cor-0D model are in good agreement with experimental data as well as those of the employed 1D model under various conditions, while the 0D model overestimates mass loss rate and particle heating rate for thermally-thick biomass particles. Considering that correction coefficients are case dependent and determination of their values are tedious, artificial neural network (ANN) was used to correlate correction coefficients as functions of convective heat transfer coefficient, particle size, gas temperature, moisture content, and particle’s dimensionless temperature to derive an ANN-Cor-0D model. Results show that the ANN-Cor-0D model has the same performance as the Cor-0D model.