文章背景与核心概要
在现代化学工程与工业设计中，工艺流程图（PFD）的手工绘制及其向管道及仪表流程图（P&ID）的转化是一项耗时且劳动力密集的工作。本文介绍了一种名为 P&ID Pilot 的端到端AI流水线，旨在彻底改变这一领域的自动化水平。
该研究创新性地将遗传算法（GA）与大语言模型（LLM）相结合，能够自动生成最优且合规的PFD拓扑结构，并执行基于源数据溯源、符合工程规范的P&ID转换。实验表明，该流水线在转换阶段实现了100%的执行成功率，为工业工艺设计的自动化提供了一种极具扩展性的解决方案。
LLMs in Process Diagram Engineering: From Optimal PFDs to Validated P&IDs
Authors: Timur Zakarin, Sergei Voitov, Sergei Shumilin, Evgeny Burnaev
Date: July 21, 2026
Identifier: arXiv:2608.11220
Subjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Summary
工艺流程图（PFD）的手工创建及其随后向管道及仪表流程图（P&ID）的转化是一个耗时且费力的过程。本研究引入了 P&ID Pilot，这是一个旨在自动化这些阶段的端到端AI流水线。通过将遗传算法（GA）与大语言模型（LLM）相结合，该系统生成最优、有效的PFD拓扑，并执行基于源接地、符合规则的向P&ID的转换。该流水线在其转换阶段实现了100%的执行成功率，为工业过程设计自动化提供了可扩展的解决方案。
The manual creation of Process Flow Diagrams (PFDs) and their subsequent conversion into Piping and Instrumentation Diagrams (P&IDs) is a time-intensive, labor-heavy process. This research introduces P&ID Pilot, an end-to-end AI pipeline designed to automate these stages. By combining genetic algorithms (GA) with Large Language Models (LLMs), the system generates optimal, valid PFD topologies and performs source-grounded, rule-compliant transformations into P&IDs. The pipeline achieves 100% execution success in its transformation phase, offering a scalable solution for industrial process design automation.
Research Overview
目前，工艺图纸的开发主要由手工工程主导。本研究探讨了人工智能在以下方面的应用： * 自动化 流程图的综合生成。 * 减少 手工劳动及相关成本。 * 优化 通过评估多种配置来优化拓扑选项。
The development of process diagrams is currently dominated by manual engineering. This study explores the application of AI to: * Automate the synthesis of flowsheet diagrams. * Reduce manual labor and associated costs. * Optimize topology options by evaluating multiple configurations.
The P&ID Pilot Pipeline
所提出的解决方案分两个不同的集成阶段运行：
The proposed solution operates in two distinct, integrated stages:
1. PFD Synthesis
第一阶段侧重于生成最优的工艺流程图。在评估了四种不同的方法后，研究人员确定 混合方法（将遗传算法与LLM集成）取得了最佳效果。该方法： * 在所有测试方法中实现了最低的损失值。 * 满足了所有要求的出口流量参数。 * 确保工程规则零违规。
The first stage focuses on generating an optimal Process Flow Diagram. After evaluating four different methodologies, the researchers determined that a hybrid approach—integrating genetic algorithms (GA) with LLMs—yielded the best results. This method: * Achieved the lowest loss value among all tested methods. * Satisfied all required outlet flow parameters. * Ensured zero violations of engineering rules.
2. P&ID Transformation
第二阶段涉及将生成的PFD修改为完全验证的P&ID。基于LLM的智能体通过以下方式管理此过程： * 利用受限制的工程软件开发工具包（SDK）来确保技术可行性。 * 生成经过验证的可执行修改。 * 严格遵守特定领域的规则和参考图结构。
The second stage involves modifying the generated PFD into a fully validated P&ID. An LLM-based agent manages this process by: * Utilizing a restricted engineering software development kit (SDK) to ensure technical feasibility. * Producing validated, executable modifications. * Maintaining strict compliance with domain-specific rules and reference graph structures.
Conclusion
P&ID Pilot流水线展示了工程自动化的重大进步。通过将GA/LLM驱动的合成与基于LLM的转换智能体相结合，系统产生了可部署、经验证的输出，大大减少了传统工艺工程中所需的手工工作量。
The P&ID Pilot pipeline demonstrates a significant advancement in engineering automation. By coupling GA/LLM-driven synthesis with an LLM-based transformation agent, the system produces deployable, validated outputs that drastically reduce the manual effort required in traditional process engineering.
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