Physics-informed neural networks for optimization of polymer reactor design

Yubin Ryu, Sunkyu Shin, Jay J. Liu, Wonbo Lee, Jonggeol Na

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Computational fluid dynamics (CFD) is utilized for chemical reactor design/analysis and enables accurate predictions by solving numerical methods. However, real-time prediction is currently infeasible due to its high computational cost. As a result, physics-informed neural networks (PINNs), which integrate data-based machine learning and physical principles, have recently emerged as a surrogate model of CFD (Raissi, M., et al. (2019)). But previous PINNs can only predict properties in terms of location or time. Here, we offer novel PINNs that can also handle operating conditions as input variables to evaluate the impact of various design variables for reactor design optimization (Raissi, M., et al. (2020)). The proposed model is applicated to an autoclave reactor for free radical polymerization of ethylene and demonstrated to be capable of interpolating/extrapolating solutions accurately under various settings.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Pages493-498
Number of pages6
DOIs
StatePublished - Jan 2023

Publication series

NameComputer Aided Chemical Engineering
Volume52
ISSN (Print)1570-7946

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

  • artificial intelligence
  • Computational fluid dynamics
  • deep learning
  • optimization
  • reactor design

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