From Text to Simulation: A Multi-Agent LLM Workflow for Automated Chemical Process Design
🤖 AI Summary
This work addresses the inefficiency of existing approaches that rely heavily on manual configuration to translate textual descriptions of chemical processes into executable simulations. The authors propose the first end-to-end automated framework, which orchestrates a multi-agent workflow powered by large language models. This framework integrates four specialized agents responsible for task comprehension, topology generation, parameter configuration, and evaluation analysis, augmented with an enhanced Monte Carlo Tree Search to improve semantic parsing accuracy and configuration robustness. For the first time, the method enables fully automatic translation from natural language to executable configurations compatible with chemical process simulation software. Evaluated on the Simona dataset, it achieves a 31.1% higher simulation convergence rate compared to the state-of-the-art baseline and reduces design time by 89.0% relative to expert manual efforts.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
process simulation
automated design
chemical engineering
text-to-simulation
workflow automation
Innovation
Methods, ideas, or system contributions that make the work stand out.
multi-agent LLM
automated process design
chemical process simulation
Enhanced Monte Carlo Tree Search
end-to-end automation
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