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.

翻译：现代过程模拟器能够实现详细的过程设计、仿真与优化；然而，构建和解读仿真模型耗时且需要专业知识。这限制了经验不足的用户进行早期探索。为解决此问题，本研究通过模型上下文协议（MCP）将大型语言模型（LLM）智能体与AVEVA过程模拟（APS）集成，实现了与严格过程仿真的自然语言交互。一套MCP服务器工具集使LLM能够通过Python与APS进行程序化通信，从而使其能够根据自然语言指令执行复杂的仿真任务。通过两个水-甲醇分离案例研究，评估了该框架在不同任务复杂度和交互模式下的表现。第一个案例表明，该智能体能够自主分析流程、发现改进机会、迭代优化、提取数据并清晰呈现结果。该框架既有利于教育目的——通过翻译技术概念和演示工作流程，也有利于经验丰富的从业者——通过自动化数据提取、加速常规任务和辅助头脑风暴。第二个案例研究通过逐步对话和单次提示两种方式评估了自主流程合成能力，展示了其对新手和专家用户的潜力。逐步模式提供了可靠、引导式的构建过程，适用于教育场景；单次提示模式则能快速构建用于后续优化的基线流程。尽管当前存在过度简化、计算错误和技术故障等局限性，仍需专家监督，但该框架在分析、优化和引导构建方面的能力表明，基于LLM的智能体有望成为有价值的协作工具。