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

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.

Figures (9)

• Figure 1: Multi-agent system architecture.
• Figure 2: Final process flow diagram for case study 1 - reaction-separation process. Blue numbers show the molar balance as pre-computed by the process development agent. Green numbers show the results from a rigorous simulation of the flowsheet built by the Chemasim modelling agent. Dashed lines indicate purge streams planned by the Chemasim modelling agent to prevent the accumulation of impurities in the recycle. If no composition is given, the process development agent assumes a pure component stream.
• Figure 3: Pressure-dependent xy-diagrams for case study 2 at selected pressures. Colors and line-styles indicate different pressures as given in the legend.
• Figure 4: Final process flow diagram for case study 2a - binary pressure-swing distillation for a minimum azeotrope. Blue numbers show the mass balance as pre-computed by the process development agent. Green numbers show the results from a rigorous simulation of the flowsheet built by the Chemasim modelling agent. Dashed lines indicate purge streams planned by the Chemasim modelling agent to prevent the accumulation of impurities in the recycle. If no composition is given, the process development agent assumes a pure component stream.
• Figure 5: Ternary diagram for the heteroazeotropic distillation systems for separating A from B in case study 3. Diagrams include the azeotropes and (ternary) miscibility gaps. Squares mark azeotropes, with MI denoting homogeneous minimum azeotropes and MIH heterogeneous minimum azeotropes. Thin lines inside the miscibility gap show tie lines connecting liquid phases in equilibrium. The critical point of the liquid-liquid equilibrium is marked with a star.