Direct Coupling of Microkinetic and Reactor Models Using Neural Networks

Klumpers, Bart; Luijten, Tim; Gerritse, Stijn; Hensen, Emiel Emiel; Filot, Ivo A. W.

doi:10.2139/ssrn.4387147

Cited by 3 publications

(3 citation statements)

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“…In parallel to this work, the Bi-Symmetric log transformation has been used by Klumpers et al for the representation of catalytic reaction rates by neural networks. 55 Power transformations pose another way to normalize the skewed distribution of wide-range data that assume both, positive and negative values. To this end, a generalized n-th root of x can be defined as gpow(x, n) = sgn(x)•|x| 1/n (31)…”

Section: Alternatives To the Hyperbolic Sinementioning

confidence: 99%

Efficient neural network models of chemical kinetics using a latent asinh rate transformation

Döppel¹,

Votsmeier²

2023

React. Chem. Eng.

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Section: Alternatives To the Hyperbolic Sinementioning

confidence: 99%

Efficient neural network models of chemical kinetics using a latent asinh rate transformation

Döppel¹,

Votsmeier²

2023

React. Chem. Eng.

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“…Bracconi and Maestri [28] and Partopour et al [29] applied random forests. First applications of neural networks as surface kinetic surrogate models were presented by Döppel and Votsmeier [30,31] and Klumpers et al [32].…”

Section: Chemical Kinetics Surrogate Models For Multi Scale Reactive ...mentioning

confidence: 99%

A neural network with embedded stoichiometry and thermodynamics for learning kinetics from reactor data

Kircher,

Döppel,

Votsmeier

2023

Preprint

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The digitalization of chemical research and industry is vastly increasing the available data for developing and parametrizing kinetic models. To exploit this data, machine learning approaches are needed that autonomously learn kinetic models from large amounts of reactor data. In this paper we develop such a tool. We present a neural network architecture that embeds thermodynamic and stoichiometric prior knowledge (STeNN) for the accurate, robust and data-efficient modelling of chemical kinetics. This network architecture is used in conjunction with neural ODEs to autonomously learn kinetic models directly from reactor data. Using the example of an adiabatic steam reformer, we demonstrate that our approach accurately recovers the true kinetics from reactor data, where conventional neural networks fail. It is further shown that the proposed framework can handle large datasets and learns kinetic models from up to 1000 reactor experiments in around ten minutes. Furthermore, due to the embedded physico-chemical knowledge, our model is robust to significant noise in the data, even in the low-data regime. We anticipate that our approach, in combination with emerging big data frameworks, will greatly increase the availability of accurate kinetic models, providing a boost to model-based reactor design and control.

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“…In parallel to this work, the Bi-Symmetric log transformation has been used by KLUMPERS et al for the representation of catalytic reaction rates by neural networks. [44] Power transformations pose another way to normalize the skewed distribution of wide-range data that assume both, positive and negative values. To this end, a generalized n-th root of x can be defined as…”

Section: Introductionmentioning

confidence: 99%

Efficient Neural Network Models of Chemical Kinetics Using a Latent asinh Rate Transformation

Döppel

Votsmeier

2023

Preprint

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We propose a new modeling strategy to build efficient neural network representations of chemical kinetics. Instead of fitting the logarithm of rates, we embed the hyperbolic sine function into neural networks and fit the actual rates. We demonstrate this approach on two detailed surface mechanisms: the preferential oxidation of CO in the presence of H2 and the ammonia oxidation under industrially relevant conditions of the Ostwald process. Implementing the surrogate models into reactor simulations shows accurate results with a speed-up of 100 000. Overall, the approach proposed in this work will significantly facilitate the application of detailed mechanistic knowledge to the simulation-based design of realistic catalytic systems. We foresee that combining this approach with neural ordinary differential equations will allow building machine learning representations of chemical kinetics directly from experimental data.

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Direct Coupling of Microkinetic and Reactor Models Using Neural Networks

Cited by 3 publications

References 50 publications

Efficient neural network models of chemical kinetics using a latent asinh rate transformation

Efficient neural network models of chemical kinetics using a latent asinh rate transformation

A neural network with embedded stoichiometry and thermodynamics for learning kinetics from reactor data

Efficient Neural Network Models of Chemical Kinetics Using a Latent asinh Rate Transformation

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