LLMs in Process Diagram Engineering: From Optimal PFDs to Validated P&IDs

arXiv:2608.11220 · cs.AI, cs.MA · Submitted 2026-07-21 · Read on arXiv

Listen

Radio episode about this paper

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "LLMs in Process Diagram Engineering: From Optimal PFDs to Validated P&IDs".

Jane: The paper was written by Timur Zakarin, Sergei Voitov, Sergei Shumilin and Evgeny Burnaev from Skolkovo Institute of Science and Technology and Artificial Intelligence Research Institute.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Title: Tom: Welcome back to the show, everyone! Today we're looking at a paper that's got a title longer than my grocery list — "LLMs in Process Diagram Engineering: From Optimal PFDs to Validated P andIDs." Jane, I need you to explain to our listeners what on earth a PFD and a P andID are, because I've been staring at this for ten minutes.

Jane: Happy to, Tom. So a PFD is a process flow diagram — it's the big-picture sketch of an industrial plant, showing the main equipment like pumps, separators, and heat exchangers, and how material flows between them. A P andID is the piping and instrumentation diagram, which is the detailed version — it adds every valve, every sensor, every control loop. Think of the PFD as the blueprint of a house and the P andID as the full wiring and plumbing schematic.

Tom: So this paper is about getting AI to draw both of those automatically? That sounds like a big deal for the chemical engineering world.

Jane: Exactly. And the authors are from Skoltech in Moscow — Timur Zakarin, Sergei Voitov, Sergei Shumilin, and Evgeny Burnaev. They've built something they call P andID Pilot, which is a full pipeline that starts from raw inlet flow properties and ends with a validated, detailed P andID.

Lu: Can I jump in here? What excites me about this paper is that they're not just asking an LLM to freehand a diagram. They've built a structured pipeline where the LLM operates within strict boundaries — a software development kit, or SDK — so every action it takes is checkable and reversible. That's a really mature way to use LLMs in engineering.

Meng: Yeah, but I want to know about the practical side. In my world, if an AI generates a diagram that's ninety-five percent right, a human still has to go through every single line to find the five percent that's wrong. Does this paper address that?

Jane: It does, Meng. That's actually the core contribution. They compare four different methods for generating the initial PFD — a multi-agent LLM system, a genetic algorithm, a reinforcement learning approach, and a hybrid that combines genetic algorithms with LLM repair. The hybrid wins with the lowest loss value and zero violations.

Tom: And that's just the first half of the pipeline. The second half takes that valid PFD and enriches it into a full P andID, adding all the valves, instruments, and control loops that the detailed diagram needs. We'll get into the numbers and the actual results in a moment, but I want to say — this feels like one of those papers where you read it and think, "Oh, this is actually going to change how plants get designed."

Lu: It's the combination of optimization and validation that makes it credible. The genetic algorithm explores thousands of topologies, and then the LLM steps in to fix the remaining issues. That's a division of labor that makes sense — brute force for search, language model for judgment.

Meng: So the question becomes, how much manual review is still needed at the end? Because that's the real bottleneck in industry.

Jane: Great question, and we'll get to that when we look at the actual results. But spoiler alert — they achieved one hundred percent execution success on their test scenarios. Let's dig into the summary next.

Summary: Tom: So we're back with "LLMs in Process Diagram Engineering: From Optimal PFDs to Validated P andIDs," and Jane just teased that the results are impressive. Let's talk about what the paper actually found.

Jane: Right. So the first stage is PFD synthesis — generating that big-picture flowsheet. They tested four methods on an oil treatment unit case. The genetic algorithm alone got a loss of about zero point eight million. The multi-agent LLM system got zero point two one million. The reinforcement learning approach was way worse at fifty-five million. But the hybrid — genetic algorithm plus LLM repair — got down to zero point zero eight nine million.

Tom: So the hybrid is roughly nine times better than the LLM-only approach and about ten times better than plain genetic algorithm. That's a huge jump.

Lu: What's interesting to me is why. The genetic algorithm is great at exploring the search space — it evaluated over seven hundred eighty thousand candidate topologies across a thousand generations. But it doesn't understand engineering rules. It might generate a topology where a pump is drawing from a gas stream, which is physically wrong. The LLM, on the other hand, understands the rules but can't explore millions of options. So the hybrid lets each do what it's good at.

Meng: But hold on — the hybrid took two thousand three hundred eighty-two seconds, which is about forty minutes. The genetic algorithm alone took five hundred five seconds. So you're paying a lot more compute for that improvement. Is it worth it?

Jane: That's a fair trade-off question. But remember, this is a design-time task, not a real-time task. Engineers spend days or weeks creating these diagrams manually. Forty minutes of compute to get a valid, near-optimal PFD is still a massive time saving.

Tom: And the cost numbers are interesting too. The hybrid produced a design with a total equipment cost of sixty-one thousand five hundred fifty. The RL approach was cheapest at seventeen thousand one hundred fifty but its PFD had topology violations — it wasn't valid. So you're comparing apples to oranges. A cheap design that doesn't work isn't really cheap.

Lu: Exactly. And that's the key insight — validity is a hard constraint, not a soft preference. The paper's loss function includes penalties for violations that are orders of magnitude larger than the cost term. So the optimizer is forced to produce something that actually works before it's allowed to optimize cost.

Meng: So what about the second stage? The PFD to P andID transformation. That's where I'm most skeptical, because that's where all the detail lives.

Jane: And that's where they did something really clever. Instead of letting the LLM directly edit a file or an image, they gave it access to a restricted SDK — a set of predefined operations like "find objects by type," "traverse connections," "insert a valve into a line." The LLM generates Python code that calls these SDK functions, and the code is executed in an isolated sandbox first.

Tom: So the LLM is essentially writing a program that modifies the diagram, and that program gets tested before it's allowed to touch the real thing.

Jane: Precisely. And they tested this on twenty-three rule-checking scenarios. The best model — Qwen3 point 5-397B — solved all twenty-three. The other two models each missed one. Then they did a full case study where they took a valid PFD of an oil treatment unit and enriched it into a P andID by applying thirteen engineering rules.

Lu: And the result was a graph with sixty-seven nodes and sixty-six edges, compared to the original twenty-nine nodes and twenty-eight edges. They introduced thirty-eight new P andID objects and reused eighteen existing ones. When they compared it against a deterministic reference implementation — zero missing nodes, zero extra nodes, zero mismatches.

Meng: That's actually remarkable. Zero differences against a reference implementation on a real case study. I was expecting at least a few edge cases where the LLM would do something unexpected.

Tom: Well, that's what the sandbox validation is for. Let's talk about how they actually built that safety layer, because I think that's the most reusable idea in this paper.

Improvements: Tom: We're back with "LLMs in Process Diagram Engineering: From Optimal PFDs to Validated P andIDs," and we've established that the results are strong. But Jane, what's the actual improvement this paper suggests over the state of the art?

Jane: The improvement is really about architecture. Previous work either used LLMs to understand existing P andIDs — like the DEXPI-based work that converts diagrams into knowledge graphs for question answering — or used reinforcement learning to generate flowsheets from scratch. This paper is the first to connect both stages into one pipeline and, more importantly, to put a safety boundary around the LLM.

Lu: I'd push back slightly on "first" — there was work on control structure prediction using graph-to-sequence models, and there was the ACPID Copilot that generates DEXPI XML from natural language. But those were single-stage. The contribution here is the full cycle and the SDK-bounded interaction model.

Meng: The SDK-bounded model is the part that gets me excited as an engineer. Because in my experience, the failure mode of LLMs in engineering is not that they're dumb — it's that they're confidently wrong. They'll generate a valve with a tag that doesn't exist in the equipment database, or connect two lines that shouldn't be connected, and they'll do it with total conviction.

Jane: Right. And the paper's answer is to make every action explicit and checkable. The LLM doesn't say "add a check valve on the discharge line." It generates code that calls `sdk.insert element(existing edge id, "CheckValve")`. That call either succeeds or fails. There's no ambiguity.

Tom: And they check at two levels, right? First, the code has to use only documented SDK operations. Second, the code has to actually execute without errors on the current graph state. So it's like a compiler check followed by a runtime check.

Lu: What I find genuinely novel is the idea of using the LLM as a repair agent for the genetic algorithm's output. The GA explores the space but doesn't understand engineering semantics. The LLM understands semantics but can't explore efficiently. By chaining them, you get the best of both. That pattern — search plus semantic repair — could generalize way beyond process diagrams.

Meng: You're thinking about other engineering domains? Like electrical schematics or structural drawings?

Lu: Absolutely. Any domain where you have a well-defined graph structure, a set of constraints, and a language model that can reason about those constraints. The SDK boundary is the key — it's what makes the LLM's output trustworthy enough to use in production.

Tom: And there's another improvement I want to highlight. The paper uses a deterministic solver to propagate flow properties through the graph — pressure, temperature, composition — and it checks for violations like cavitation risk in pumps or pressure limits in valves. So the LLM isn't just drawing a pretty diagram; it's drawing a diagram that passes physics checks.

Jane: That's the part that makes this practical rather than academic. The solver isn't a toy — it handles branching flows, merging flows, gas separation with efficiency curves, water separation with mass balance equations, pump head curves, heat exchanger heat transfer. It's a real engineering calculation engine.

Meng: So when they say "validated P andID," they mean the process actually works, not just that the symbols are in the right places.

Jane: Exactly. And that's the improvement over prior work. The ACPID Copilot generated DEXPI XML, but it didn't validate whether the process made physical sense. This paper closes that gap.

Tom: So where does this leave us? We've got a pipeline that generates optimal PFDs and then enriches them into validated P andIDs. What's the bigger picture here? Let's bring in Lalam to think about the cultural and societal implications.

Conclusion: Tom: So we've spent this whole episode on "LLMs in Process Diagram Engineering: From Optimal PFDs to Validated P andIDs." Let's wrap this up. Jane, give us the one-sentence version.

Jane: One sentence? This paper shows that by combining genetic algorithms for search with LLMs for semantic repair, and by constraining the LLM through a validated SDK, you can automate the entire journey from raw process requirements to a physics-checked, rule-compliant P andID — with zero differences against a reference implementation on a real case study.

Lu: And I'd add that the architecture is the real contribution. The idea of separating language understanding from diagram modification — letting the LLM propose, but letting deterministic code dispose — is a pattern that will outlive this specific application.

Meng: From my side, the practical impact is clear. This doesn't replace the engineer, but it replaces the most tedious part of the engineer's job. Instead of spending days placing valves and checking that every pump has isolation valves, the engineer reviews the AI's work and focuses on the hard design decisions.

Lalam: If I may step in — the cultural impact here is about trust. For years, engineers have been told that AI will take their jobs, or that AI is too unreliable for safety-critical work. This paper demonstrates a middle path: AI as a tireless assistant that works within boundaries, produces verifiable output, and gets checked by deterministic tools. That's the model that will actually get adopted in industry, because it respects the engineer's need for control.

Tom: That's a good way to put it. And I want to mention the numbers one more time because they're worth repeating — twenty-three out of twenty-three rule-checking scenarios solved by the best model, thirty-eight new P andID objects introduced with zero mismatches against the reference, and a loss value of zero point zero eight nine million for the hybrid PFD generator, which is an order of magnitude better than the alternatives.

Jane: And the limitations are honest too. They tested on one process — oil treatment — with a specific set of thirteen enrichment rules. They're not claiming this works for every chemical plant on Earth. But the framework is general, and the validation methodology is sound.

Lu: I'd love to see this extended to more complex processes — distillation columns, reactors, recycle loops with multiple control strategies. And I'd love to see the SDK extended to handle more P andID elements like relief valves, control valves with fail-safe positions, and interlock logic.

Meng: The compute cost is worth mentioning too. The hybrid took about forty minutes. That's fine for design-time, but if you wanted to explore hundreds of design alternatives interactively, you'd need to optimize the GA part significantly.

Tom: Good point. But for now, this paper is a strong proof of concept that the full cycle is achievable. We'll be watching to see if the authors release the code or extend this to more case studies.

Jane: And with that, we'll say goodbye to "LLMs in Process Diagram Engineering: From Optimal PFDs to Validated P andIDs." Thanks for listening, and we'll see you next time with another paper from the arXiv.

Tom: Stay curious, everyone.

Timur Zakarin, Sergei Voitov, Sergei Shumilin, Evgeny Burnaev

Skolkovo Institute of Science and Technology · Artificial Intelligence Research Institute

cs.AI, cs.MA

Submitted: 2026-07-21

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

Importance score: 61/100

The gist: From Optimal PFDs to Validated P&IDs," presents a full-cycle AI pipeline for automating the creation and enrichment of process engineering diagrams.

Key concepts

PFD (Process Flow Diagram)
A high-level sketch of an industrial plant showing the main equipment like pumps and heat exchangers. It illustrates the overall process and how materials flow between major components.
P&ID (Piping and Instrumentation Diagram)
The detailed engineering diagram that adds every specific component, including valves, sensors, control loops, and piping. It is a comprehensive schematic of the entire system.
LLMs in Process Diagram Engineering
A method using Large Language Models to automate the creation of industrial diagrams. The process involves generating optimal PFDs and then transforming them into validated P&IDs within a structured, safety-checked pipeline.
SDK-bounded Interaction Model
A safety architecture where the LLM cannot directly edit files or images. Instead, it generates code that calls predefined functions (the SDK), ensuring every action is explicit, checkable, and reversible.

Terminology

Summary

Summary

The paper, "LLMs in Process Diagram Engineering: From Optimal PFDs to Validated P&IDs, presents a full-cycle AI pipeline for automating the creation and enrichment of process engineering diagrams. The authors state that the creation of a process flow diagram (PFD) and its subsequent transformation into a piping and instrumentation diagram (P&ID) is predominantly performed manually, and they propose a pipeline called P&ID Pilot to address this. The pipeline is described as a practical end-to-end AI pipeline capable of handling flowsheet developing for both stages, where The first stage focuses on PFD synthesis, whereas the second is directed toward modifying the generated PFD into P&ID."

The paper's contribution is threefold: "First, we present a pipeline that connects optimal PFD synthesis with subsequent P&ID-oriented interaction. Second, we compare several approaches to PFD generation within a graph-based process-design setting. Third, we introduce a controlled LLM-based workflow for P&ID analysis and modification, and evaluate it on domain-grounded executable rules over a graph representation of a P&ID."

For the first stage, optimal PFD synthesis, the authors compare four methods: a multi-agent system (MAS) approach, a genetic algorithm (GA), a reinforcement learning with graph convolutional neural networks (RL/GCNN) approach, and a hybrid GA/LLM approach. The test case is generating the optimal PFD for an oil treatment unit (OTU), which involves gas separation, water separation, pressure increase, and flow heating. A custom Python solver is used to propagate flow properties through all nodes of the graph and check constraints.

The MAS approach uses a group of LLM agents: an Optimization Agent, a Cypher Validation Agent, and a Scheme Validation Agent. The system uses Neo4j for graph storage and Cypher queries for modifications. The authors tested four LLMs (Qwen3.6-35B-A3B, gpt-oss:120B, Qwen3.5-397B-A17B-FP8, DeepSeek-V4-Pro) and found that the best results are demonstrated by the Qwen3.6-35B-A3B and gpt-oss:120B models, while the larger models struggled to converge and provide a valid generated PFD.

The GA approach uses the PyGAD package and evaluates thousands of chromosomes over generations. The RL/GCNN approach is inspired by prior work and uses an LLM-as-a-reward policy. The hybrid approach combines GA for initial topology generation with an LLM (gpt-oss:120) to fix any detected errors provided by the deterministic validator.

The results comparison shows that the valid PFD with no violations that comes closest to all the target flow properties was produced by the hybrid approach. The loss values for the methods are: GA (Baseline) at 0.808 × 106, MAS at 0.213 × 106, RL/GCNN at 55.939 × 106, and Hybrid (GA/LLM) at 0.089 × 106. The hybrid approach achieved the Lowest outlet deviation while the other methods had Higher outlet deviation or violations.

For the second stage, PFD-to-P&ID enrichment, the paper introduces an SDK-bounded interaction model: an LLM interprets a user request and generates executable actions, while all access to the diagram is mediated by predefined graph operations and validation procedures. The P&ID is represented as a directed attributed graph where "Nodes correspond to diagram objects, including equipment units, pumps, valves, sensors, pipe elements, connection points, and auxiliary entities. Edges encode relations between these objects, such as physical connections, flow direction, control links, and other logical dependencies."

The workflow involves the user sending a natural-language request, the LLM generating a Python action program using only documented SDK operations, validation of the program in an isolated environment, and then application to the working graph after user confirmation. The paper emphasizes that the LLM does not directly edit an image, XML file, CAD file, or serialized graph. It proposes a sequence of bounded operations whose validity can be checked by software.

The evaluation uses a test set of 23 scenarios based on domain-grounded engineering rules. The results show that "All tested models produced syntactically correct code for every scenario and executed it successfully. The differences appear only at the level of semantic correctness. The best model solved all 23 scenarios, while the two other models each failed on one scenario." Specifically, Qwen3.5-397B-A17B-FP8 achieved 100.00% accuracy, while Qwen3.6-35B-A3B and DeepSeek-V4-Pro each achieved 95.65%.

The second experiment evaluates PFD-to-P&ID transformation for the oil-treatment case study. The input PFD contains "two centrifugal pumps, four heat exchangers, a gas separator, a water separator, a pipe tee, existing isolation valves, and off-page connectors. Its node-link representation contains 29 nodes and 28 directed process edges. Thirteen atomic rules were applied, derived from a P&ID project standard, Hydraulic Institute pump guidance, and API RP 12J separator practice." The rules cover requirements such as isolation valves, strainers, pressure indicators, check valves, temperature indicators, level instrumentation, vents, drains, and pressure-relief connections.

The results show that "The LLM-generated programs introduced 38 P&ID objects and reused 18 objects already present in the PFD. The resulting graph contained 67 nodes and 66 edges. Comparison with the deterministic reference found no missing or additional nodes, no semantic object-type mismatches, and no differences in either process topology or equipment-attachment relations."

The paper concludes that "a hybrid GA/LLM approach generates optimal, rule-compliant PFD topologies, while the LLM-based transformation agent, constrained by the SDK, reliably produces source-grounded P&ID modifications. The authors state that By integrating these two stages within a single workflow, the proposed framework indeed may reduce manual engineering effort and accelerate the exploration of alternative design configurations. Crucially, the built-in validation mechanisms against domain-specific rules and graph structures ensure that the generated outputs are not only novel but also practically deployable in real-world engineering settings."

The paper also acknowledges limitations: "The evaluation was conducted in two controlled settings: a benchmark of 23 rule-checking scenarios and a PFD-to-P&ID transformation for an oil-treatment process. Although the generated graph matched the reference structure, this result is limited to the selected engineering rules, equipment types, and process topology. Additional experiments are required to assess the generalizability of the approach to other processes and project-specific requirements. Moreover, the experiment evaluates graph-level consistency rather than the completeness of a construction-ready P&ID. Detailed safety studies, equipment sizing, and final engineering validation remain outside the scope of this work. The evaluation focuses on semantic and topological correctness; graphical sheet layout is outside its scope and is not included in the reported comparisons."

Improvements for AI systems

Based on the paper, here are the specific improvements I can implement in an AI system, along with the resulting capabilities:

Improvement: Replace pure LLM generation or pure genetic algorithm with a two-stage pipeline: GA explores thousands of topology candidates deterministically, then an LLM repair agent fixes residual violations using validator feedback.

Resulting capability: The system generates process flow diagrams (PFDs) that are fully rule-compliant (zero topological/physical violations) and achieve the lowest loss value (0.089 × 106) compared to GA alone (0.808 × 106), MAS (0.213 × 106), or RL/GCNN (55.939 × 106).

Improvement: Constrain the LLM to interact with a graph model only through a predefined software development kit (SDK) with documented primitives (find, traverse, add, insert, attach). Validate every generated action program in an isolated sandbox before committing changes.

Improvement: Implement a Python solver that propagates flow properties (pressure, temperature, composition) through each equipment node using explicit equations (pump head curves, heat exchanger heat balance, separator efficiencies, pipe tee splitting, valve pressure limits). Store violations at node level for loss computation.

Improvement: Use a three-agent architecture: Optimization Agent (RAG-based equipment retrieval + plan generation), Cypher Validation Agent (syntax/logic checking), and Scheme Validation Agent (post-execution topology/physics checks). Iterate until valid.

Improvement: Replace hand-crafted reward functions with an LLM that evaluates the quality of generated flowsheets, integrated into an actor-critic RL framework with GCNN-based graph embeddings.

Improvement: Apply 13 atomic rules derived from KLM project standards, Hydraulic Institute pump guidance, and API RP 12J to transform a PFD into a P&ID. Verify each modification against a deterministic reference for object type, placement, and connectivity.

Improvement: Represent P&IDs as directed attributed graphs (nodes = equipment/instruments/valves, edges = connections/control links) rather than raw CAD/XML files. This makes topology, attributes, and paths uniformly queryable.

Improvement: After each LLM-generated modification, run deterministic validation and return structured error messages (e.g., flowmeter is located after the flow-control valve, bypass path exists around ESV) to the LLM for revision.

  1. Generate optimal, rule-compliant PFDs from inlet/outlet flow specifications and an equipment database, exploring thousands of topologies in under 40 minutes.

  2. Transform a PFD into a source-grounded P&ID automatically, adding required instrumentation, isolation, vents, drains, and relief connections without manual intervention.

  3. Answer natural-language queries about P&ID compliance (e.g., Is there a bypass around the fire-isolation valve?) with 95–100% accuracy across 23 diverse rule-checking scenarios.

  4. Execute modifications safely—every change is validated in an isolated environment, and only after passing SDK compliance and physics checks is it committed to the working diagram.

  5. Provide structured, actionable feedback to engineers, listing specific violations (e.g., pump inlet pressure below minimum, missing check valve on discharge line) rather than vague error messages.

  6. Scale across model sizes—the system works with models from 35B to 397B parameters, with smaller models often outperforming larger ones when paired with deterministic validation.

  7. Preserve engineering context—stream types, fluid properties, nominal diameters, and flow attributes are maintained when edges are split or objects are inserted.

  8. Reduce manual engineering effort by automating repetitive but non-trivial tasks: equipment flanking, valve placement, instrumentation attachment, and control-loop consistency checks.

These improvements collectively enable a full-cycle AI pipeline that can take a natural-language process description, generate an optimal PFD, enrich it into a validated P&ID, and answer compliance questions—all with deterministic verification at every step.

Abstract

Nowadays, the creation of a process flow diagram (PFD) and its subsequent transformation into a piping and instrumentation diagram (P&ID) is predominantly performed manually. Applying artificial intelligence in the task could potentially lead not only to process automation and time savings, but also to financial gains by exploring numerous diagram's topology options and reducing manual labor. This research presents P&ID Pilot - a practical end-to-end AI pipeline capable of handling flowsheet developing for both stages. The first stage focuses on PFD synthesis, whereas the second is directed toward modifying the generated PFD into P&ID. After comparing four different methods, the hybrid approach combining genetic algorithms (GA) and large language models (LLM) is shown to generate the optimal valid PFD topology, achieving the lowest loss value among all the methods, while satisfying the required outlet flow parameters without engineering-rule violations. For the second stage, the proposed LLM-based agent successfully transforms the generated PFD into a source-grounded P&ID by producing validated, executable modifications through a restricted engineering software development kit, achieving 100% execution success while maintaining compliance with domain-specific rules and reference graph structures. This unified pipeline - coupling GA/LLM-driven synthesis with an LLM-based transformation agent - offers a feasible path toward end-to-end process design automation by producing validated, deployable outputs and substantially reduces manual engineering effort.

Sources

Related papers