LLM-guided Chemical Process Optimization with a Multi-Agent Approach
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912652681281536 |
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| author | Zeng, Tong Badrinarayanan, Srivathsan Ock, Janghoon Lai, Cheng-Kai Farimani, Amir Barati |
| author_facet | Zeng, Tong Badrinarayanan, Srivathsan Ock, Janghoon Lai, Cheng-Kai Farimani, Amir Barati |
| contents | Chemical process optimization maximizes production efficiency and economic performance, but optimization algorithms, including gradient-based solvers, numerical methods, and parameter grid searches, become impractical when operating constraints are ill-defined or unavailable. We present a multi-agent LLM framework that autonomously infers operating constraints from minimal process descriptions, then collaboratively guides optimization. Our AutoGen-based framework employs OpenAI's o3 model with specialized agents for constraint generation, parameter validation, simulation, and optimization guidance. Through autonomous constraint generation and iterative multi-agent optimization, the framework eliminates the need for predefined operational bounds. Validated on hydrodealkylation across cost, yield, and yield-to-cost ratio metrics, the framework achieved competitive performance with conventional methods while reducing wall-time 31-fold relative to grid search, converging in under 20 minutes. The reasoning-guided search demonstrates sophisticated process understanding, correctly identifying utility trade-offs and applying domain-informed heuristics. Unlike conventional methods requiring predefined constraints, our approach uniquely combines autonomous constraint generation with interpretable parameter exploration. Model comparison reveals reasoning-capable architectures (o3, o1) are essential for successful optimization, while standard models fail to converge. This approach is particularly valuable for emerging processes and retrofit applications where operational constraints are poorly characterized or unavailable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20921 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM-guided Chemical Process Optimization with a Multi-Agent Approach Zeng, Tong Badrinarayanan, Srivathsan Ock, Janghoon Lai, Cheng-Kai Farimani, Amir Barati Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science Chemical process optimization maximizes production efficiency and economic performance, but optimization algorithms, including gradient-based solvers, numerical methods, and parameter grid searches, become impractical when operating constraints are ill-defined or unavailable. We present a multi-agent LLM framework that autonomously infers operating constraints from minimal process descriptions, then collaboratively guides optimization. Our AutoGen-based framework employs OpenAI's o3 model with specialized agents for constraint generation, parameter validation, simulation, and optimization guidance. Through autonomous constraint generation and iterative multi-agent optimization, the framework eliminates the need for predefined operational bounds. Validated on hydrodealkylation across cost, yield, and yield-to-cost ratio metrics, the framework achieved competitive performance with conventional methods while reducing wall-time 31-fold relative to grid search, converging in under 20 minutes. The reasoning-guided search demonstrates sophisticated process understanding, correctly identifying utility trade-offs and applying domain-informed heuristics. Unlike conventional methods requiring predefined constraints, our approach uniquely combines autonomous constraint generation with interpretable parameter exploration. Model comparison reveals reasoning-capable architectures (o3, o1) are essential for successful optimization, while standard models fail to converge. This approach is particularly valuable for emerging processes and retrofit applications where operational constraints are poorly characterized or unavailable. |
| title | LLM-guided Chemical Process Optimization with a Multi-Agent Approach |
| topic | Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2506.20921 |