CeProAgents: A Hierarchical Agents System for Automated Chemical Process Development
🤖 AI Summary
Chemical process development remains heavily reliant on expert intuition, posing significant challenges to automation. To address this, this work proposes a hierarchical multi-agent architecture tailored for chemical engineering, comprising specialized agents in knowledge, conceptualization, and parameterization that collaboratively integrate dynamic dialogue groups with structured workflows to enable automatic task decomposition and execution. The study introduces CeProBench—the first multidimensional benchmark for evaluating chemical process development systems—and demonstrates the proposed framework’s effectiveness and superiority across its six representative tasks. These results highlight both the transformative potential and current limitations of large language models in industrial chemical engineering applications.
Application Category
📝 Abstract
The development of chemical processes, a cornerstone of chemical engineering, presents formidable challenges due to its multi-faceted nature, integrating specialized knowledge, conceptual design, and parametric simulation. Capitalizing on this, we propose CeProAgents, a hierarchical multi-agent system designed to automate the development of chemical process through collaborative division of labor. Our architecture comprises three specialized agent cohorts focused on knowledge, concept, and parameter respectively. To effectively adapt to the inherent complexity of chemical tasks, each cohort employs a novel hybrid architecture that integrates dynamic agent chatgroups with structured agentic workflows. To rigorously evaluate the system, we establish CeProBench, a multi-dimensional benchmark structured around three core pillars of chemical engineering. We design six distinct types of tasks across these dimensions to holistically assess the comprehensive capabilities of the system in chemical process development. The results not only confirm the effectiveness and superiority of our proposed approach but also reveal the transformative potential as well as the current boundaries of Large Language Models (LLMs) for industrial chemical engineering.
Problem
Research questions and friction points this paper is trying to address.
chemical process development
multi-agent system
automation
chemical engineering
complexity
Innovation
Methods, ideas, or system contributions that make the work stand out.
hierarchical multi-agent system
chemical process development
dynamic agent chatgroups
structured agentic workflows
CeProBench
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