Proceedings of ESCAPE 35ISSN: 2818-4734
Volume: 4 (2025)
Table of Contents
LAPSE:2025.0371v1
Published Article
LAPSE:2025.0371v1
pyDEXPI: A Python framework for piping and instrumentation diagrams (P&IDs) using the DEXPI information model
Dominik P. Goldstein, Lukas Schulze Balhorn, Achmad Anggawirya Alimin, Artur M. Schweidtmann
June 27, 2025
Abstract
Developing piping and instrumentation diagrams (P&IDs) is a fundamental task in process engineering. For designing complex installations, such as petroleum plants, multiple departments across several companies are involved in refining and updating these diagrams, creating significant challenges in data exchange between different software platforms from various vendors. The primary challenge in this context is interoperability, which refers to the seamless exchange and interpretation of information to collectively pursue shared objectives. To enhance the P&ID creation process, a unified, machine-readable data format for P&ID data is essential. A promising candidate is the Data Exchange in the Process Industry (DEXPI) standard. We present pyDEXPI, an open-source implementation of the DEXPI format for P&IDs in Python. pyDEXPI makes P&ID data more efficient to handle, more flexible, and more interoperable. We envision that, with further development, pyDEXPI will act as a central scientific computing library for the domain of digital process engineering, facilitating interoperability and the application of data analytics and generative artificial intelligence on P&IDs. We provide the pyDEXPI package with the documentation on GitHub at https://github.com/process-intelligence-research/pyDEXPI.
Keywords
Data model, DEXPI, FAIR data, Open-source, Piping and instrumentation diagram, Software toolbox
Suggested Citation
Goldstein DP, Balhorn LS, Alimin AA, Schweidtmann AM. pyDEXPI: A Python framework for piping and instrumentation diagrams (P&IDs) using the DEXPI information model. Systems and Control Transactions 4:1365-1370 (2025) https://doi.org/10.69997/sct.139043
Author Affiliations
Goldstein DP: Process Intelligence Research Group, Department of Chemical Engineering, Delft University of Technology, Van der Maasweg 9, Delft 2629 HZ, The Netherlands
Balhorn LS: Process Intelligence Research Group, Department of Chemical Engineering, Delft University of Technology, Van der Maasweg 9, Delft 2629 HZ, The Netherlands
Alimin AA: Process Intelligence Research Group, Department of Chemical Engineering, Delft University of Technology, Van der Maasweg 9, Delft 2629 HZ, The Netherlands
Schweidtmann AM: Process Intelligence Research Group, Department of Chemical Engineering, Delft University of Technology, Van der Maasweg 9, Delft 2629 HZ, The Netherlands
Journal Name
Systems and Control Transactions
Volume
4
First Page
1365
Last Page
1370
Year
2025
Publication Date
2025-07-01
Version Comments
Original Submission
Other Meta
PII: 1365-1370-1253-SCT-4-2025, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2025.0371v1
This Record
External Link

https://doi.org/10.69997/sct.139043
Article DOI
Download
Files
Jun 27, 2025
Main Article
License
CC BY-SA 4.0
Meta
Record Statistics
Record Views
2670
Version History
[v1] (Original Submission)
Jun 27, 2025
 
Verified by curator on
Jun 27, 2025
This Version Number
v1
Citations
Most Recent
This Version
URL Here
http://psecommunity.org/LAPSE:2025.0371v1
 
Record Owner
PSE Press
Links to Related Works
Directly Related to This Work
Article DOI
References Cited
  1. Towler GP, Sinnott RK. Chemical Engineering Design. Elsevier/Butterworth-Heinemann (2008)
  2. Toghraei M. Piping and Instrumentation Diagram Development, First Edition. John Wiley & Sons, Inc. (2019) https://doi.org/10.1002/9781119329503
  3. Schweidtmann AM. Generative artificial intelligence in chemical engineering. Nature Chemical Engineering 1:193-193 (2024) https://doi.org/10.1038/s44286-024-00041-5
  4. Oeing J, Welscher W, Krink N, Jansen L, Henke F, Kockmann N. Using artificial intelligence to support the drawing of piping and instrumentation diagrams using DEXPI standard. Digital Chemical Engineering 4:100038 (2022) https://doi.org/10.1016/j.dche.2022.100038
  5. Nabil T, Le Moullec Y, Le Coz A. Machine learning based design of a supercritical CO2 concentrating solar power plant. In: Conference Proceedings of the European sCO2 Conference 3rd European Conference on Supercritical CO2 (sCO2) Power Systems 2019: 19th-20th September 2019. 148-157 (2019)
  6. Gowaikar S, Iyengar S, Segal S, Kalyanaraman S. An agentic approach to automatic creation of P&ID diagrams from natural language descriptions. arXiv preprint (2024)
  7. Schulze Balhorn L, Caballero M, Schweidtmann AM. Toward autocorrection of chemical process flowsheets using large language models. In 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering. 3109-3114 (2025) https://doi.org/10.1016/B978-0-443-28824-1.50519-6
  8. Vogel G, Schulze Balhorn L, Schweidtmann AM. Learning from flowsheets: a generative transformer model for autocompletion of flowsheets. Computers & Chemical Engineering 171:108162 (2023) https://doi.org/10.1016/j.compchemeng.2023.108162
  9. Hirtreiter E, Schulze Balhorn L, Schweidtmann AM. Toward automatic generation of control structures for process flow diagrams with large language models. AIChE Journal 70(1) (2023) https://doi.org/10.1002/aic.18259
  10. Theißen M, Wiedau M. DEXPI P&ID Specification. DEXPI Initiative (2021)
  11. Proteus XML. Proteus schema for P&ID exchange. https://github.com/ProteusXML/proteusxml
  12. pnb plants & bytes GmbH. PID Verificator 1.0.1 for DEXPI 1.3. Software (2025) https://www.plants-and-bytes.de/en/p-id-and-dexpi
  13. Colvin S. Pydantic. https://github.com/pydantic/pydantic
  14. Vogel G, Hirtreiter E, Schulze Balhorn L, Schweidtmann AM. SFILES 2.0: an extended text-based flowsheet representation. Optimization and Engineering 24:2911-2933 (2023) https://doi.org/10.1007/s11081-023-09798-9
  15. Theisen MF, Flores KN, Schulze Balhorn L, Schweidtmann AM. Digitization of chemical process flow diagrams using deep convolutional neural networks. Digital Chemical Engineering 6:100072 (2023) https://doi.org/10.1016/j.dche.2022.100072
  16. Alimin AA, Goldstein DP, Schulze Balhorn L, Schweidtmann AM. ChatP&ID: talking to P&IDs through large language models and knowledge graphs. In: ESCAPE35 - Proceedings of the 35th European Symposium on Computer Aided Process Engineering, Ghent, Belgium. (2025)
  17. Schulze Balhorn L, Seijsener N, Dao K, Kim M, Goldstein DP, Driessen GHM, Schweidtmann AM. Rule-based autocorrection of piping and instrumentation diagrams (P&IDs) on graphs. In: ESCAPE35 - Proceedings of the 35th European Symposium on Computer Aided Process Engineering, Ghent, Belgium. (2025)