Context is all you need: Towards autonomous model-based process design using agentic AI in flowsheet simulations

TL;DR

This paper introduces an agentic AI framework that leverages large language models and multi-agent systems to assist in chemical process flowsheet simulation and design, demonstrating effectiveness on complex industrial examples.

Contribution

It presents a novel multi-agent AI system integrating LLMs for autonomous process modelling in flowsheet simulations, a largely unexplored area in chemical engineering.

Findings

Effective generation of process model code using LLMs

Successful application to reaction, distillation, and entrainer selection tasks

Discussion of current limitations and future research directions

Abstract

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.