CRAFTS：面向化工过程仿真的大语言模型智能体协同角色自适应微调

中文摘要 AI 辅助

构建可执行的化工过程模型仍需大量人工操作。化工工程师会将不明确的需求转化为关于单元操作、热力学、物流、规格、自由度（DoF）、初始化、求解器修复及优化的耦合决策，一处错误就可能导致模型失效。CRAFTS 模仿化工工程师的分阶段工作流程，将仿真构建分解为有界子任务，分配给7个有界角色，阶段间设置确定性的 IDAES/Pyomo 闸门。给定自然语言需求、工艺流程图（PFD）证据及精选化工知识后，输入理解与意图模块会提取需求、约束及工艺语义；视觉、拓扑和规格专家将其转化为类型化仿真器合约；调试与优化模块支持有界修复与合格优化。微调应用于3个架构关键的视觉、拓扑和规格角色，其余角色使用未微调的 Qwen。生成的 VisualGraphIR、TopologyIR、SpecIR、BuildPlan 和 SolveReport 会公开单元、端口、热力学、数值及执行决策。仅当语义构件通过工程闸门后，才会附加兼容的构造器、属性包和运行器。我们推出 OpenIDAES-450，这是一个包含450个 IDAES 过程仿真案例的数据集，并通过对其冻结的82个保留案例拆分进行求解和合格优化，评估完整的7角色 LangChain/LangGraph 工作流。CRAFTS 为91.5%的案例完成了规定的验证与执行合约，且单元、物流和定向连接的 F1 值分别达到0.815、0.791和0.782。这些结果证明了角色专业化、类型化中间表示及确定性工程闸门在可靠自动化过程模型构建中的有效性。

英文摘要

Constructing an executable chemical-process model remains manually intensive. Chemical engineers translate underspecified requests into coupled decisions about unit operations, thermodynamics, streams, specifications, degrees of freedom (DoF), initialization, solver repair, and optimization; one error can invalidate the model. CRAFTS mirrors the staged workflow of chemical engineers by decomposing simulation building into bounded subtasks assigned to seven bounded roles, with deterministic IDAES/Pyomo gates between stages. Given a natural-language request, process flowsheet diagram (PFD) evidence, and curated chemical-engineering knowledge, Input Understanding and Intent recover requirements, constraints, and process semantics; visual, topology, and specification specialists translate them into typed simulator contracts; and Debug and Optimization support bounded repair and eligible optimization. Fine-tuning is applied to the three schema-critical visual, topology, and specification roles, while the remaining roles use untuned Qwen. The resulting VisualGraphIR, TopologyIR, SpecIR, BuildPlan, and SolveReport expose unit, port, thermodynamic, numerical, and execution decisions. Compatible constructors, property packages, and runners are attached only after semantic artifacts pass engineering gates. We introduce OpenIDAES-450, a 450-case IDAES process- simulation dataset, and evaluate the complete seven-role LangChain/LangGraph workflow through solve and eligible optimization on its frozen 82-case held-out split. CRAFTS completes the prescribed validation and execution contract for for 91.5% of cases and achieves unit, stream, and directed-connection F1 scores of 0.815, 0.791, and 0.782. These results demonstrate the effectiveness of role specialization, typed intermediate representations, and deterministic engineering gates for reliable automated process-model construction.

发表机构

• National University of Singapore（新加坡国立大学）
• Xiamen University（厦门大学）

机构由 AI 辅助整理，请以论文原文为准。