Data augmentation for machine learning of chemical process flowsheets

Lukas Schulze Balhorn, Edwin Hirtreiter, Lynn Luderer, Artur M. Schweidtmann

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientific

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Artificial intelligence has great potential for accelerating the design and engineering of chemical processes. Recently, we have shown that transformer-based language models can learn to auto-complete chemical process flowsheets using the SFILES 2.0 string notation. Also, we showed that language translation models can be used to translate Process Flow Diagrams (PFDs) into Process and Instrumentation Diagrams (P&IDs). However, artificial intelligence methods require big data and flowsheet data is currently limited. To mitigate this challenge of limited data, we propose a new data augmentation methodology for flowsheet data that is represented in the SFILES 2.0 notation. We show that the proposed data augmentation improves the performance of artificial intelligence-based process design models. In our case study flowsheet data augmentation improved the prediction uncertainty of the flowsheet autocompletion model by 14.7%. In the future, our flowsheet data augmentation can be used for other machine learning algorithms on chemical process flowsheets that are based on SFILES notation.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
EditorsAntonis Kokossis, Michael C. Georgiadis, Efstratios N. Pistikopoulos
ISBN (Electronic)978-0-443-15274-0
Publication statusPublished - 2023

Publication series

NameComputer Aided Chemical Engineering
ISSN (Print)1570-7946

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project Otherwise 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.


  • Data Augmentation
  • Flowsheet Autocompletion
  • Transformers


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