TY - CHAP
T1 - Data augmentation for machine learning of chemical process flowsheets
AU - Balhorn, Lukas Schulze
AU - Hirtreiter, Edwin
AU - Luderer, Lynn
AU - Schweidtmann, Artur M.
N1 - 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-care 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.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Data Augmentation
KW - Flowsheet Autocompletion
KW - SFILES
KW - Transformers
UR - http://www.scopus.com/inward/record.url?scp=85165103757&partnerID=8YFLogxK
U2 - 10.1016/B978-0-443-15274-0.50320-6
DO - 10.1016/B978-0-443-15274-0.50320-6
M3 - Chapter
AN - SCOPUS:85165103757
T3 - Computer Aided Chemical Engineering
SP - 2011
EP - 2016
BT - Computer Aided Chemical Engineering
A2 - Kokossis, Antonis
A2 - Georgiadis, Michael C.
A2 - Pistikopoulos, Efstratios N.
PB - Elsevier
ER -