Towards automatic generation of Piping and Instrumentation Diagrams (P&IDs) with Artificial Intelligence

• URL: http://arxiv.org/abs/2211.05583v1
• Date: Wed, 26 Oct 2022 10:03:15 GMT
• Title: Towards automatic generation of Piping and Instrumentation Diagrams (P&IDs) with Artificial Intelligence
• Authors: Edwin Hirtreiter and Lukas Schulze Balhorn and Artur M. Schweidtmann
• Abstract summary: We propose a novel, completely data-driven method for the prediction of control structures. We cast the control structure prediction as a translation task where Process Flow Diagrams (PFDs) are translated to 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing Piping and Instrumentation Diagrams (P&IDs) is a crucial step during the development of chemical processes. Currently, this is a tedious, manual, and time-consuming task. We propose a novel, completely 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 our recently proposed SFILES 2.0 notation. Model training is performed in a transfer learning approach. Firstly, 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 of a larger P&IDs dataset for industry applications.

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