Autonomous Kinetic Modeling of Biomass Pyrolysis using Chemical Reaction Neural Networks

05/24/2021
by   Weiqi Ji, et al.
0

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.

READ FULL TEXT

page 13

page 22

research
02/20/2020

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...
research
11/30/2020

Kinetics-Informed Neural Networks

Chemical kinetics consists of the phenomenological framework for the dis...
research
11/09/2020

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

Recently developed physics-informed neural network (PINN) has achieved s...
research
06/12/2023

Using a neural network approach to accelerate disequilibrium chemistry calculations in exoplanet atmospheres

In this era of exoplanet characterisation with JWST, the need for a fast...
research
06/23/2023

Stress Testing BERT Anaphora Resolution Models for Reaction Extraction in Chemical Patents

The high volume of published chemical patents and the importance of a ti...
research
11/17/2018

Chemical Structure Elucidation from Mass Spectrometry by Matching Substructures

Chemical structure elucidation is a serious bottleneck in analytical che...
research
01/17/2021

Data-driven discovery of multiscale chemical reactions governed by the law of mass action

In this paper, we propose a data-driven method to discover multiscale ch...

Please sign up or login with your details

Forgot password? Click here to reset