Abstract
Multiscale modeling of catalytical chemical reactors typically results in solving a system of partial differential equations (PDEs) or ordinary differential equations (ODEs). Despite significant progress, the numerical solution of such PDE or ODE systems is still a computational bottleneck. In the past, deep learning techniques have gained attention for developing surrogate models in chemical engineering. Also, hybrid models and physics-informed neural networks (PINNs) have been developed to integrate physical knowledge and data-driven approaches. However, it is often unclear how such modeling approaches compare for specific case studies. In this study, we investigate and compare state-of-the-art surrogate and hybrid models for the spatial evolution of the state variables in a packetbed reactor for methanol production. Firstly, we develop a tailored hybrid model based on PINNs, thereby seamlessly integrating physical knowledge and data. Secondly, we investigate a recently-developed time-series transformer model to learn the spatial evolution of the state variables. As a benchmark model, we train a traditional multilayer perceptron (MLP) and compare the models to a standard numerical integration technique. We achieve orders of magnitude in speedup using MLPs and PINNs when compared to classical ODE solvers, while maintaining high levels of accuracy in modeling the underlying system.
Original language | English |
---|---|
Title of host publication | Proceedings of the 34th European Symposium on Computer Aided Process Engineering |
Subtitle of host publication | 15th International Symposium on Process Systems Engineering (ESCAPE34/PSE24) |
Editors | Flavio Manenti, Gintaras V. Reklaitis |
Place of Publication | Amsterdam/Kidlington/Cambridge, MA |
Publisher | Elsevier |
Pages | 571-576 |
Number of pages | 6 |
ISBN (Print) | 978-0-443-33897-7, 978-0-443-28824-1 |
DOIs | |
Publication status | Published - 2024 |
Event | 34th European Symposium on Computer-Aided Process Engineering / 15th International Symposium on Process Systems Engineering - Palazzo dei Congressi - Villa Vittoria, Florence, Italy Duration: 2 Jun 2024 → 6 Jun 2024 https://www.aidic.it/escape34-pse24 |
Publication series
Name | Computer Aided Chemical Engineering |
---|---|
Publisher | Elsevier |
Volume | 53 |
ISSN (Print) | 1570-7946 |
Conference
Conference | 34th European Symposium on Computer-Aided Process Engineering / 15th International Symposium on Process Systems Engineering |
---|---|
Abbreviated title | ESCAPE34 - PSE24 |
Country/Territory | Italy |
City | Florence |
Period | 2/06/24 → 6/06/24 |
Internet address |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- hybrid modeling
- physics-informed machine learning
- reactor modeling
- time-series transformer