Autonomous Kinetic Modeling of Biomass Pyrolysis using Chemical Reaction Neural Networks

by   Weiqi Ji, et al.

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 fully 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.



There are no comments yet.


page 13

page 22


Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network

The inference of chemical reaction networks is an important task in unde...

Kinetics-Informed Neural Networks

Chemical kinetics consists of the phenomenological framework for the dis...

Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics

Recently developed physics-informed neural network (PINN) has achieved s...

ChemNODE: A Neural Ordinary Differential Equations Approach for Chemical Kinetics Solvers

The main bottleneck when performing computational fluid dynamics (CFD) s...

Chemical Structure Elucidation from Mass Spectrometry by Matching Substructures

Chemical structure elucidation is a serious bottleneck in analytical che...

Predicting the Stereoselectivity of Chemical Transformations by Machine Learning

Stereoselective reactions (both chemical and enzymatic reactions) have b...

Stiff Neural Ordinary Differential Equations

Neural Ordinary Differential Equations (ODE) are a promising approach to...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.