  Modeling the burning processes of biomass such as wood, grass, and crops is
crucial for the modeling and prediction of wildland and urban fire behavior.
Despite its importance, the burning of solid fuels remains poorly understood,
which can be partly attributed to the unknown chemical kinetics of most solid
fuels. Most available kinetic models were built upon expert knowledge, which
requires chemical insights and years of experience. This work presents a
framework for autonomously discovering biomass pyrolysis kinetic models from
thermogravimetric analyzer (TGA) experimental data using the recently developed
chemical reaction neural networks (CRNN). The approach incorporated the CRNN
model into the framework of neural ordinary differential equations to predict
the residual mass in TGA data. In addition to the flexibility of
neural-network-based models, the learned CRNN model is interpretable, by
incorporating the fundamental physics laws, such as the law of mass action and
Arrhenius law, into the neural network structure. The learned CRNN model can
then be translated into the classical forms of biomass chemical kinetic models,
which facilitates the extraction of chemical insights and the integration of
the kinetic model into large-scale fire simulations. We demonstrated the
effectiveness of the framework in predicting the pyrolysis and oxidation of
cellulose. This successful demonstration opens the possibility of rapid and
autonomous chemical kinetic modeling of solid fuels, such as wildfire fuels and
industrial polymers.
