发表机构
Skoltech; AIRI（斯科尔科沃科技学院; 人工智能研究院（AIRI））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出P&ID Pilot端到端AI流水线，结合GA与LLM生成最优PFD，再通过基于LLM的智能体将其转换为100%执行成功的合规P&ID，实现工艺设计自动化并减少人工工作量。
AI 中文摘要
如今，工艺流程图（PFD）的创建及其后续转换为管道及仪表流程图（P&ID）的工作主要由人工完成。将人工智能应用于该任务不仅有望实现流程自动化、节省时间，还能通过探索大量图表拓扑选项、减少人工劳动带来经济效益。本研究提出了P&ID Pilot——一种实用的端到端AI流水线，能够处理两个阶段的工艺图开发工作。第一阶段专注于PFD合成，第二阶段则致力于将生成的PFD修改为P&ID。在比较四种不同方法后，结合遗传算法（GA）与大语言模型（LLM）的混合方法被证实可生成最优的有效PFD拓扑，在所有方法中实现最低损失值，同时满足所需的出口流量参数且不违反工程规则。对于第二阶段，所提出的基于LLM的智能体通过受限制的工程软件开发工具包生成经验证、可执行的修改，成功将生成的PFD转换为基于源的P&ID，实现100%的执行成功率，同时保持符合特定领域规则和参考图结构。该统一流水线——结合GA/LLM驱动的合成与基于LLM的转换智能体——通过生成经验证、可部署的输出，为端到端工艺设计自动化提供了可行路径，并大幅减少了人工工程工作量。
英文摘要
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.
