Solution and Parameter Identification of a Fixed-Bed Reactor Model for Catalytic CO<sub>2</sub> Methanation Using Physics-Informed Neural Networks

Solution and Parameter Identification of a Fixed-Bed Reactor Model for Catalytic CO<sub>2</sub> Methanation Using Physics-Informed Neural Networks

In this study, we develop physics-informed neural networks (PINNs) to solve an isothermal fixed-bed (IFB) model for catalytic CO<sub>2</sub> methanation. The PINN includes a feed-forward artificial neural network (FF-ANN) and physics-informed constraints, such as governing equations, bou...

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Bibliographic Details
Main Authors: Son Ich Ngo, Young-Il Lim
Format: Article
Language:English
Published: MDPI AG 2021-10-01
Series:Catalysts
Subjects:
catalytic CO<sub>2</sub> methanation
fixed-bed reactor
reaction kinetics
system identification
machine learning
physics-informed neural network
Online Access:https://www.mdpi.com/2073-4344/11/11/1304

Internet

https://www.mdpi.com/2073-4344/11/11/1304

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