Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Graph Learning in Machine‐Readable Plant Topology Data
Digitalization 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.
Graph Learning in Machine‐Readable Plant Topology Data
Digitalization 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.
Graph Learning in Machine‐Readable Plant Topology Data
Oeing, Jonas (Autor:in) / Brandt, Kevin (Autor:in) / Wiedau, Michael (Autor:in) / Tolksdorf, Gregor (Autor:in) / Welscher, Wolfgang (Autor:in) / Kockmann, Norbert (Autor:in)
Chemie Ingenieur Technik ; 95 ; 1049-1060
01.07.2023
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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