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Applications & References

Python framework for Piping and Instrumentation Diagrams (P&IDs) using the DEXPI information model

Overview

pyDEXPI has been used in various research applications focusing on Generative AI for process engineering, P&ID analysis, error correction, and process development. This page provides an overview of key applications and relevant publications.

Vision of GenAI for Process Engineering

Smart, machine-readable P&ID data enables innovative applications of Generative Artificial Intelligence in chemical and process engineering.

Chat Interface for P&IDs using Large Language Models

Interactive interfaces that allow engineers to query and interact with P&IDs using natural language through Large Language Models.

  • Alimin, A. A., & Schweidtmann, A. M. (2026). GraphRAG for Engineering Diagrams: ChatP&ID Enables LLM Interaction with P&IDs. arXiv preprint arXiv:2603.22528.
    https://doi.org/10.48550/arXiv.2603.22528

  • Alimin, A. A., Goldstein, D. P., Schulze Balhorn, L., & Schweidtmann, A. M. (2025). Talking like piping and instrumentation diagrams (P&IDs). Proceedings of the 35th European Symposium on Computer Aided Process Engineering (ESCAPE35), Ghent, Belgium.
    https://doi.org/10.69997/sct.159477

Error Correction of P&IDs

Automated detection and correction of errors in P&ID drawings using rule-based systems and machine learning approaches.

  • Schulze Balhorn, L., Seijsener, N., Dao, K., Kim, M., Goldstein, D. P., Driessen, G. H., & Schweidtmann, A. M. (2025). Rule-based autocorrection of Piping and Instrumentation Diagrams (P&IDs) on graphs. Proceedings of the 35th European Symposium on Computer Aided Process Engineering (ESCAPE35), Ghent, Belgium.
    https://doi.org/10.69997/sct.150968

  • Schulze Balhorn, L., Caballero, M., & Schweidtmann, A. M. (2024). Toward autocorrection of chemical process flowsheets using large language models. In Computer Aided Chemical Engineering (Vol. 53, pp. 3109-3114). Elsevier.
    https://doi.org/10.1016/B978-0-443-28824-1.50519-6

Process Development

Applications of machine learning and generative AI for process flowsheet development, completion, and control structure generation.

  • Vogel, G., Schulze Balhorn, L., & Schweidtmann, A. M. (2023). Learning from flowsheets: A generative transformer model for autocompletion of flowsheets. Computers & Chemical Engineering, 171, 108162.
    https://doi.org/10.1016/j.compchemeng.2023.108162

  • Schulze Balhorn, L., Hirtreiter, E., Luderer, L., & Schweidtmann, A. M. (2023). Data augmentation for machine learning of chemical process flowsheets. In Computer Aided Chemical Engineering (Vol. 52, pp. 2011-2016). Elsevier.
    https://doi.org/10.1016/B978-0-443-15274-0.50320-6

  • Hirtreiter, E., Schulze Balhorn, L., & Schweidtmann, A. M. (2024). Toward automatic generation of control structures for process flow diagrams with large language models. AIChE Journal, 70(1), e18259.
    https://doi.org/10.1002/aic.18259

  • Schulze Balhorn, L., Degens, K., & Schweidtmann, A. M. (2025). Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence. Computers & Chemical Engineering, 109121.
    https://doi.org/10.1016/j.compchemeng.2025.109121

Digitization of Paper P&IDs to Smart P&IDs

Converting legacy paper-based P&IDs into machine-readable smart P&IDs using deep learning and computer vision techniques.