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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.01654 |
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| _version_ | 1866912937137930240 |
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| author | Yang, Yuhang Li, Ruikang Ma, Jifei Zhang, Kai Liu, Qi Han, Jianyu Bu, Yonggan Zhou, Jibin Lian, Defu Li, Xin Chen, Enhong |
| author_facet | Yang, Yuhang Li, Ruikang Ma, Jifei Zhang, Kai Liu, Qi Han, Jianyu Bu, Yonggan Zhou, Jibin Lian, Defu Li, Xin Chen, Enhong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01654 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CeProAgents: A Hierarchical Agents System for Automated Chemical Process Development Yang, Yuhang Li, Ruikang Ma, Jifei Zhang, Kai Liu, Qi Han, Jianyu Bu, Yonggan Zhou, Jibin Lian, Defu Li, Xin Chen, Enhong Artificial Intelligence 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. |
| title | CeProAgents: A Hierarchical Agents System for Automated Chemical Process Development |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.01654 |