Graph learning in machine‐readable plant topology data

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dc.contributor.authorOeing, Jonas
dc.contributor.authorBrandt, Kevin
dc.contributor.authorWiedau, Michael
dc.contributor.authorTolksdorf, Gregor
dc.contributor.authorWelscher, Wolfgang
dc.contributor.authorKockmann, Norbert
dc.date.accessioned2025-01-23T09:25:25Z
dc.date.available2025-01-23T09:25:25Z
dc.date.issued2023-05-03
dc.description.abstractDigitalization 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.en
dc.identifier.urihttp://hdl.handle.net/2003/43379
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-25211
dc.language.isoen
dc.relation.ispartofseriesChemie - Ingenieur - Technik; 95(7)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectArtificial intelligenceen
dc.subjectData managementen
dc.subjectDEXPIen
dc.subjectGraph neural networksen
dc.subjectPiping & instrumentation diagramen
dc.subjectProcess industryen
dc.subject.ddc660
dc.titleGraph learning in machine‐readable plant topology dataen
dc.typeText
dc.type.publicationtypeResearchArticle
dcterms.accessRightsopen access
eldorado.secondarypublicationtrue
eldorado.secondarypublication.primarycitationOeing, J., Brandt, K., Wiedau, M., Tolksdorf, G., Welscher, W. and Kockmann, N. (2023), Graph Learning in Machine-Readable Plant Topology Data. Chemie Ingenieur Technik, 95: 1049-1060. https://doi.org/10.1002/cite.202200223
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1002/cite.202200223

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