arXiv:2502.18493v2 Announce Type: replace-cross Abstract: A piping and instrumentation diagram (P&ID) is a central reference document in chemical process engineering. Currently, chemical engineers manually review P&IDs through visual inspection to find and rectify errors. However, engineering projects can involve hundreds to thousands of P&ID pages, creating a significant revision workload. This study proposes a rule-based method to support engineers with error detection and correction in P&IDs. The method is based on a graph representation of P&IDs, enabling automated error detection and corr
The proliferation of more sophisticated AI models and graph-based computational methods is enabling automation in historically manual and complex engineering tasks.
This development allows for significant efficiency gains and error reduction in critical industrial processes, impacting high-stakes sectors like chemical engineering.
Manual, highly-skilled visual inspection of complex engineering diagrams can now be partially automated by AI agents, reducing human workload and potential for error.
- · Chemical Engineering Firms
- · Industrial Automation Software Providers
- · AI/ML Developers
- · Process Engineers
- · Traditional Manual Review Services
Reduced project timelines and costs due to faster, more accurate P&ID review.
Increased safety and operational reliability in chemical plants through fewer design and operational errors.
Potential for broader AI integration into other complex industrial diagram analysis and design, leading to further automation across engineering disciplines.
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Read at arXiv cs.AI