Oeing, JonasBrandt, KevinWiedau, MichaelTolksdorf, GregorWelscher, WolfgangKockmann, Norbert2025-01-232025-01-232023-05-03http://hdl.handle.net/2003/4337910.17877/DE290R-25211Digitalization shows that data and its exchange are indispensable for a versatile and sustainable process industry. There must be a shift from a document-oriented to a data-oriented process industry. Standards for the harmonization of data structures play an essential role in this change. In engineering, DEXPI (Data Exchange in the Process Industry) is already a well-developed, machine-readable data standard for describing piping and instrumentation diagrams (P&ID). In this publication, industry, software vendors, and research institutions have joined forces to demonstrate the current developments and potentials of machine-readable P&IDs in the DEXPI format combined with artificial intelligence. The aim is to use graph neural networks to learn patterns in machine-readable P&ID data, which results in the efficient engineering and development of new P&IDs.enChemie - Ingenieur - Technik; 95(7)https://creativecommons.org/licenses/by/4.0/Artificial intelligenceData managementDEXPIGraph neural networksPiping & instrumentation diagramProcess industry660ResearchArticle