The Challenge of P&ID Interpretation

Piping and Instrumentation Diagrams (P&IDs) are among the most information-dense documents used in industry. They capture equipment, instrumentation, control logic, and interconnections — but they do so in a form designed for human interpretation. Engineers rely on these diagrams for discussion, analysis, communication, and decision-making at every stage of a facility's life cycle.

Other formats exist for storing process data, including machine-friendly representations and digital twins. Yet, in practice, the P&IDs always contain the latest and most authoritative information. Design changes, field adjustments, and safety updates are reflected there first. They remain the central and enduring reference for how a process is actually configured and understood by humans.

From Digitization to Interpretation

The focus of this work was on evaluating the feasibility of leveraging that human-encoded information to answer real process questions. A well-drawn P&ID represents a significant investment of expertise. Every connection, symbol, and note reflects an engineer's intent. The challenge is to make this information accessible to AI systems in a way that preserves that intent and enables contextual reasoning.

Most research to date has focused on digitizing P&IDs—recognizing symbols, tracing pipelines, and generating structured digital versions of drawings. These efforts are valuable, but they typically stop at representation. The next step is interpretation: enabling AI systems to reason over that information and understand the functional relationships between components.

The Role of Vision-Language Models

Interpreting P&IDs requires identifying how elements interact: what flows where, what controls what, and under what conditions. These relationships are often implicit, conveyed through layout, labeling, and standardized conventions that human readers interpret almost instinctively. For an AI model to be useful in process safety or design applications, it must learn to interpret those same cues.

Recent advances in vision-language models (VLMs) and thinking/reasoning models have made it possible to combine visual and textual understanding in a single system. In the context of P&IDs, this means connecting diagrammatic structure with engineering semantics.

The Training Data Challenge

Developing such models depends on access to high-quality, domain-specific training data. General-purpose AI systems are not extensively trained on engineering drawings and cannot be expected to infer process semantics from unrelated image or text datasets. And yet, even these public models can read some information from P&IDs—hinting at how we're on the cusp of AI reaching that capability.

Constructing datasets from real P&IDs with verified annotations for connectivity, equipment, and control logic allows models to learn directly from the way engineers encode information. The goal is not to automate design, but to make existing human knowledge machine-usable while preserving the fidelity of the original engineering meaning.

Validation and Trust in AI Systems

Before founding Scitrera, I spent over a decade at Gexcon, where much of my work involved using the validated computational fluid dynamics (CFD) tool FLACS to predict gas dispersion and explosion consequences. In CFD, credibility is built through a structured process of verification and validation. Every model is tested against experimental benchmarks and analyzed for accuracy, repeatability, and limits of applicability.

AI systems that produce or interpret data used in safety decisions should be held to a similar standard. Without well-defined evaluation protocols, there is no consistent way to measure whether an AI model's conclusions are correct or trustworthy.

Future Applications and Integration

Once AI systems can reliably interpret P&IDs, they can become a foundational component within larger engineering and safety workflows. The same data that supports a HAZOP today could tomorrow inform automated design reviews, cross-check maintenance modifications, or provide live feedback within a digital twin.

By making the human-encoded structure of P&IDs accessible to AI, we can integrate accurate, up-to-date process context into analytical and decision-support systems — reducing the gap between documented intent and operational reality.

Conclusion

P&IDs remain the definitive record of process design intent — dense with human-encoded information, continuously updated, and central to safe operation. Making that data accessible to AI does not replace human expertise; it extends it.

As generative AI models mature, their success in process safety and design applications will depend less on raw capability and more on how well they are trained and validated. Borrowing from established practices in CFD and other computational sciences, we can create a framework for AI that is rigorous, transparent, and grounded in engineering reality.

This approach ensures that when AI systems read a P&ID, the conclusions they draw can be trusted as part of the same decision-making process that humans have relied on for decades.