用AI代理自动设计化工流程，让工程师省力。
Context is all you need: Towards autonomous model-based process design using agentic AI in flowsheet simulations
- 用大模型理解文档和示例，自动生成合规代码。
- 多智能体协作：一个想方案，一个写代码，完成全流程建模。
- 已在反应分离、变压精馏等场景验证，适合化工研发人员。
将具备推理与工具使用能力的智能体人工智能系统（Agentic AI）集成大型语言模型（LLMs），正在改变多个领域，尤其是在软件开发中。然而，其在化学过程流程图建模中的应用仍基本未被探索。本文提出一种智能体AI框架，可在工业级流程模拟环境中提供辅助。我们展示了GitHub Copilot（GitHub, Inc., 2026）结合先进LLM如Claude Opus 4.6（Anthropic, PBC, 2026），仅通过技术文档和少量注释示例作为上下文，即可生成符合内部流程建模工具Chemasim语法的代码。基于此，我们构建了一个多智能体系统：一个智能体利用工程知识解决抽象问题，另一个智能体将其转化为Chemasim代码实现。我们在典型流程建模案例中验证了该框架的有效性，包括（i）反应/分离过程，（ii）变压精馏，以及（iii）包含夹带剂选择的非均相共沸精馏。同时讨论了当前局限，并展望未来改进方向。
原文摘要 · Abstract (English)
Agentic AI systems integrating large language models (LLMs) with reasoning and tooluse capabilities are transforming various domains - in particular, software development. In contrast, their application in chemical process flowsheet modelling remains largely unexplored. In this work, we present an agentic AI framework that delivers assistance in an industrial flowsheet simulation environment. To this end, we show the capabilities of GitHub Copilot (GitHub, Inc., 2026), when using state-of-the-art LLMs, such as Claude Opus 4.6 (Anthropic, PBC, 2026), to generate valid syntax for our in-house process modelling tool Chemasim using the technical documentation and a few commented examples as context. Based on this, we develop a multi-agent system that decomposes process development tasks with one agent solving the abstract problem using engineering knowledge and another agent implementing the solution as Chemasim code. We demonstrate the effectiveness of our framework for typical flowsheet modelling examples, including (i) a reaction/separation process, (ii) a pressure-swing distillation, and (iii) a heteroazeotropic distillation including entrainer selection. Along these lines, we discuss current limitations of the framework and outline future research directions to further enhance its capabilities.
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