Applying LLMs to complex industrial processes remains challenging due to the semantic gap between natural language design intents and the rigorous physical logic of engineering. In the field of petroleum refining engineering, a critical bottleneck is the automated synthesis of Unit-level Process Diagrams (UPDs), which serve as the topological bridge connecting abstract requirements to concrete unit operations. In this paper, we propose RefineGPT, a domain-specialized agent for autonomous refinery design.RefineGPT adopts a hierarchical architecture in which a supervised fine-tuned small language model is responsible for selecting units that satisfy design requirements, while a large language model is used to connect these units to generate the final topology. To enable supervised training, we develop a pipeline that extracts latent process motifs from noisy, unstructured legacy topologies and synthesizes high-quality rationale-based Chain-of-Thought (CoT) training data. Empirical validation demonstrates that RefineGPT achieves substantial improvements in topological consistency and chemical engineering feasibility, establishing a high-fidelity pathway for AI-augmented industrial process synthesis.

翻译：将大型语言模型应用于复杂工业过程仍具有挑战性，原因在于自然语言设计意图与工程严谨物理逻辑之间存在语义鸿沟。在石油炼制工程领域，一个关键瓶颈在于单元级工艺流程图（UPD）的自动合成——该流程图作为连接抽象需求与具体单元操作的拓扑桥梁。本文提出RefineGPT，一种面向炼油厂自主设计的领域专用智能体。RefineGPT采用分层架构，其中经监督微调的小语言模型负责选择满足设计要求的单元，而大语言模型则用于连接这些单元以生成最终拓扑结构。为实现监督训练，我们开发了一套流水线，从含噪非结构化的遗留拓扑中提取潜在工艺基元，并合成高质量的基于推理链（CoT）训练数据。实证验证表明，RefineGPT在拓扑一致性和化学工程可行性方面取得显著提升，为人工智能增强的工业过程合成建立了高保真路径。