Graph learning in machine‐readable plant topology data
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Date
2023-05-03
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Abstract
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.
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Keywords
Artificial intelligence, Data management, DEXPI, Graph neural networks, Piping & instrumentation diagram, Process industry