From P&ID Drawings to Process Graphs: A Multimodal Language Model Approach

Abstract

Piping and instrumentation diagrams (P&IDs) encode the functional structure of process plants and are a critical yet underutilised source of engineering knowledge for digital twins and intelli-gent decision support. However, digitising legacy P&IDs remains challenging due to heterogene-ous drawing standards and the reliance of existing methods on brittle symbol recognition and rule-based connectivity reconstruction. This work reframes P&ID digitization as the extraction of equipment tags and inference of process topology, rather than graphical reproduction. We pro-pose a two-stage workflow based on multimodal large language models, in which visual extrac-tion and topology reconstruction are treated as distinct reasoning stages guided by chemical en-gineering process knowledge. The approach is evaluated on two ANSI-standard P&ID case stud-ies of increasing complexity. Results show that decomposing visual extraction and topology rea-soning yields more accurate and structurally consistent process representations than end-to-end digitization, highlighting the potential of language-model-based, knowledge-guided workflows for scalable and semantically reliable P&ID digitization.