用大模型让化学流程模拟变简单，自然语言就能操作。
Large Language Model Agent for User-friendly Chemical Process Simulations
- 大模型通过Python接口与仿真软件对话，理解自然语言指令执行任务。
- 在两个甲醇-水分离案例中实现自动优化、数据提取和流程设计。
- 适合新手学习和专家提速，降低仿真门槛。
现代过程模拟器可实现详细的设计、仿真与优化，但构建和解读模拟过程耗时且需专业知识，限制了非专业人士的早期探索。为此，本文将大语言模型（LLM）通过模型上下文协议（MCP）集成到AVEVA Process Simulation（APS）中，实现与严格过程模拟的自然语言交互。基于MCP的服务器工具集使LLM可通过Python程序化调用APS，从自然语言指令中执行复杂仿真任务。通过两个甲醇-水分离案例评估该框架在不同任务复杂度和交互模式下的表现。第一个案例中，代理能自主分析流程图、发现改进机会、迭代优化，并清晰提取数据与呈现结果。该框架既可用于教学，帮助解释技术概念与演示流程；也适用于经验丰富的从业者，自动化数据提取、加速常规任务并支持头脑风暴。第二个案例评估了通过逐步对话或单次提示实现的自主流程合成，分别适用于教育场景和快速生成可后续优化的基准流程。尽管存在简化过度、计算错误和技术故障等局限，仍需专家监督，但其在分析、优化与引导式构建方面的表现表明，基于大模型的代理有望成为重要协作工具。
原文摘要 · Abstract (English)
Modern process simulators enable detailed process design, simulation, and optimization; however, constructing and interpreting simulations is time-consuming and requires expert knowledge. This limits early exploration by inexperienced users. To address this, a large language model (LLM) agent is integrated with AVEVA Process Simulation (APS) via Model Context Protocol (MCP), allowing natural language interaction with rigorous process simulations. An MCP server toolset enables the LLM to communicate programmatically with APS using Python, allowing it to execute complex simulation tasks from plain-language instructions. Two water-methanol separation case studies assess the framework across different task complexities and interaction modes. The first shows the agent autonomously analyzing flowsheets, finding improvement opportunities, and iteratively optimizing, extracting data, and presenting results clearly. The framework benefits both educational purposes, by translating technical concepts and demonstrating workflows, and experienced practitioners by automating data extraction, speeding routine tasks, and supporting brainstorming. The second case study assesses autonomous flowsheet synthesis through both a step-by-step dialogue and a single prompt, demonstrating its potential for novices and experts alike. The step-by-step mode gives reliable, guided construction suitable for educational contexts; the single-prompt mode constructs fast baseline flowsheets for later refinement. While current limitations such as oversimplification, calculation errors, and technical hiccups mean expert oversight is still needed, the framework's capabilities in analysis, optimization, and guided construction suggest LLM-based agents can become valuable collaborators.
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