Automatic generation of P&IDs with Artificial Intelligence

Developing Piping and Instrumentation Diagrams (P&IDs) is a crucial step during process development. We propose a data-driven method for the prediction of control structures. Our methodology is inspired by end-to-end transformer-based human language translation models. We cast the control structure prediction as a translation task where Process Flow Diagrams (PFDs) are translated to P&IDs. To use established transformer-based language translation models, we represent the P&IDs and PFDs as strings using the SFILES 2.0 notation. We pre-train our model using generated P&IDs to learn the grammatical structure of the process diagrams. Thereafter, the model is fine-tuned leveraging transfer learning on real P&IDs. The model achieved a top-5 accuracy of 74.8% on 10,000 generated P&IDs and 89.2% on 100,000 generated P&IDs. These promising results show great potential for AI-assisted process engineering. The tests on a dataset of 312 real P&IDs indicate the need for a larger P&IDs dataset for industry applications.

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Relate publications

  • Vogel, G., Balhorn, L. S., Hirtreiter, E., & Schweidtmann, A. M. (2022). SFILES 2.0: An extended text-based flowsheet representation. arXiv preprint arXiv:2208.00778.
  • Vogel, G., Balhorn, L. S., & Schweidtmann, A. M. (2022). Learning from flowsheets: A generative transformer model for autocompletion of flowsheets. arXiv preprint arXiv:2208.00859.