  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.
