Solution and Parameter Identification of a Fixed-Bed Reactor Model for Catalytic CO2 Methanation Using Physics-Informed Neural Networks

Ngo, Son Ich; Lim, Young‐Il

doi:10.3390/catal11111304

Cited by 28 publications

(14 citation statements)

References 39 publications

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“…Recent advances in physics-informed learning have demonstrated their potential in dealing with inverse problems in chemical reactions, such as unknown parameter identification and reaction network discovery, which can greatly improve our understanding of complex reaction processes and lead to more accurate and reliable models. For instance, Ngo et al 34 applied inverse PINN methods to obtain the unknown parameter (effectiveness factor) of a highly nonlinear reaction rate model for catalytic CO 2 methanation in an isothermal fixed-bed reactor. The results showed that it was able to identify an unknown effectiveness factor with an error of 0.3%, even for a small number of observation datasets.…”

Section: Chemical Reactionmentioning

confidence: 99%

The Application of Physics-Informed Machine Learning in Multiphysics Modeling in Chemical Engineering

Wu,

Wang,

et al. 2023

Ind. Eng. Chem. Res.

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Physics-Informed Machine Learning (PIML) is an emerging computing paradigm that offers a new approach to tackle multiphysics modeling problems prevalent in the field of chemical engineering. These problems often involve complex transport processes, nonlinear reaction kinetics, and multiphysics coupling. This Review provides a detailed account of the main contributions of PIML with a specific emphasis on modeling momentum transfer, heat transfer, mass transfer, and chemical reactions. The progress in method development (e.g., algorithm and architecture), software libraries, and specific applications (e.g., multiphysics coupling and surrogate modeling) are detailed. On this basis, future challenges highlight the importance of developing more practical solutions and strategies for PIML, including turbulence models, domain decomposition, training acceleration, surrogate modeling, hybrid modeling, and geometry module creation.

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Section: Chemical Reactionmentioning

confidence: 99%

The Application of Physics-Informed Machine Learning in Multiphysics Modeling in Chemical Engineering

Wu,

Wang,

et al. 2023

Ind. Eng. Chem. Res.

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“…(a) PINN without transport effect, able to solve both inverse and forward kinetic problems (Gusmão et al); (b) PINN with transport effect in a fixed bed reactor for catalytic CO 2 methanation, able to solve inverse kinetic problems (Ngo and Lim). Figure 18a was adapted with permission from ref .…”

Section: Current Status and Challengesmentioning

confidence: 99%

“…Another typical example is illustrated in Figure b, where the classic reactor model is used to consider the transport effect in an isothermal fixed bed reactor for catalytic CO 2 methanation. Ngo and Lim trained an effective physics-informed feed-forward ANN. In particular, a classic plug-flow fixed bed reactor model was introduced to consider the effect of transport phenomena, and the unknown effectiveness factor existing in reaction kinetics can be estimated with a 0.3% error even for a limited number of data sets.…”

Section: Current Status and Challengesmentioning

confidence: 99%

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu

Chen

Ouyang

et al. 2022

Ind. Eng. Chem. Res.

120

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Artificial intelligence (AI), machine learning (ML), and data science are leading to a promising transformative paradigm. ML, especially deep learning and physics-informed ML, is a valuable toolkit that complements incomplete domain-specific knowledge in conventional experimental and computational methods. ML can provide flexible techniques to facilitate the conceptual development of new robust predictive models for multiphase flows and reactors by finding hidden pattern/information/mechanism in a data set. Due to such emergence, we thereby comprehensively survey, explore, analyze, and discuss key advancements of recent ML applications to hydrodynamics, heat and mass transfer, and reactions in single-phase and multiphase flow systems from different aspects: (1) development of multiphase closure models of drag force, turbulence stresses and heat/mass transfer to improve the accuracy and efficiency of typical CFD simulations; (2) image reconstruction, regime identification, key parameter predictions, and optimization of multiphase flow and transport fields; (3) reaction kinetics modeling (e.g., predictions of reaction networks, kinetic parameters, and species production) and reaction condition optimization. These sections also discuss and analyze the key advantages and weakness of ML for solving the problems in the domain of multiphase flows and reactors. Finally, we summarize the under-solving challenges and opportunities in order to identify future directions that would be useful for the research community. Future development and study of multiphase flows and reactors are envisaged to be accelerated by ML and data science.

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“…However, the study on this matter in the engineering area is currently still at the stage of using ordinary DNN [25][26] and time series forecasting models such as LSTM [27]. On the other hand, all existing data-driven methods for CODES are based on PINN and therefore cannot handle parametric CODES [6,28,29]. Following the concept of PINO, we aim to propose a physicsinformed neural operator PINO-MBD for CODES in MBD.…”

Section: Machine Learning-based Pde Solversmentioning

confidence: 99%

PINO-MBD: Physics-informed Neural Operator for Solving Coupled ODEs in Multi-body Dynamics

Ding

Tong

et al. 2022

Preprint

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In multi-body dynamics, the motion of a complicated physical object is described as a coupled ordinary differential equation system with multiple unknown solutions. Engineers need to constantly adjust the object to meet requirements at the design stage, where a highly efficient solver is needed. The rise of machine learning-based partial differential equation solvers can meet this need. These solvers can be classified into two categories: approximating the solution function (Physics-informed neural network) and learning the solution operator (Neural operator). The recently proposed physics-informed neural operator (PINO) gains advantages from both categories by embedding physics equations into the loss function of a neural operator. Following this state-of-art concept, we propose the physics-informed neural operator for coupled ODEs in multi-body dynamics (PINO-MBD), which learns the mapping between parameter spaces and solution spaces. Once PINO-MBD is trained, only one forward pass of the network is required to obtain the solutions for a new instance with different parameters. To handle the difficulty that coupled ODEs contain multiple solutions (instead of only one in normal PDE problems), two new physics embedding methods are also proposed. The experimental results on classic vehicle-track coupled dynamics problem show state-of-art performance not only on solutions but also the first and second derivatives of solutions.

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Solution and Parameter Identification of a Fixed-Bed Reactor Model for Catalytic CO2 Methanation Using Physics-Informed Neural Networks

Cited by 28 publications

References 39 publications

The Application of Physics-Informed Machine Learning in Multiphysics Modeling in Chemical Engineering

The Application of Physics-Informed Machine Learning in Multiphysics Modeling in Chemical Engineering

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

PINO-MBD: Physics-informed Neural Operator for Solving Coupled ODEs in Multi-body Dynamics

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