From Text to Simulation: A Multi-Agent LLM Workflow for Automated Chemical Process Design

TL;DR

This paper introduces a multi-agent LLM-based workflow that automates chemical process simulation from textual descriptions, significantly improving convergence rates and reducing manual design time, thus advancing AI-assisted chemical engineering design.

Contribution

The work presents a novel multi-agent system leveraging LLMs and Monte Carlo Tree Search for end-to-end automated chemical process simulation from text, a significant step beyond existing methods.

Findings

Achieves 31.1% higher simulation convergence rate than baselines.

Reduces chemical process design time by 89%.

Demonstrates applicability across diverse industries.

Abstract

Process simulation is a critical cornerstone of chemical engineering design. Current automated chemical design methodologies focus mainly on various representations of process flow diagrams. However, transforming these diagrams into executable simulation flowsheets remains a time-consuming and labor-intensive endeavor, requiring extensive manual parameter configuration within simulation software. In this work, we propose a novel multi-agent workflow that leverages the semantic understanding capabilities of large language models(LLMs) and enables iterative interactions with chemical process simulation software, achieving end-to-end automated simulation from textual process specifications to computationally validated software configurations for design enhancement. Our approach integrates four specialized agents responsible for task understanding, topology generation, parameter configuration, and evaluation analysis, respectively, coupled with Enhanced Monte Carlo Tree Search to accurately interpret semantics and robustly generate configurations. Evaluated on Simona, a large-scale process description dataset, our method achieves a 31.1% improvement in the simulation convergence rate compared to state-of-the-art baselines and reduces the design time by 89. 0% compared to the expert manual design. This work demonstrates the potential of AI-assisted chemical process design, which bridges the gap between conceptual design and practical implementation. Our workflow is applicable to diverse process-oriented industries, including pharmaceuticals, petrochemicals, food processing, and manufacturing, offering a generalizable solution for automated process design.