LLM-guided chemical process optimization with a multi-agent approach
Tianqin Zeng, Srivathsan Badrinarayanan, Janghoon Ock, Cheng-Kai Lai, Amir Barati Farimani
Abstract
Abstract Chemical process optimization is crucial to maximize production efficiency and economic performance. Optimization algorithms, including gradient-based solvers, numerical methods, and parameter grid searches, become impractical when operating constraints are ill-defined or unavailable, requiring engineers to rely on subjective heuristics to estimate feasible parameter ranges. To address this constraint definition bottleneck, we present a multi-agent framework of large language model (LLM) agents that autonomously infer operating constraints from minimal process descriptions, then collaboratively guide optimization using the inferred constraints. Our AutoGen-based agentic framework employs OpenAI’s o3 model, with specialized agents for constraint generation, parameter validation, simulation execution, and optimization guidance. Through two phases: (i) autonomous constraint generation using embedded domain knowledge, and (ii) iterative multi-agent optimization, the framework eliminates the need for predefined operational bounds. Validated on the hydrodealkylation process across cost, yield, and yield-to-cost ratio metrics, the framework demonstrated competitive performance with conventional optimization methods while achieving a 31-fold reduction in wall-time relative to grid search, converging in under 20 min and requiring far fewer iterations to converge. Beyond computational efficiency, the framework’s reasoning-guided search demonstrates sophisticated process understanding, correctly identifying utility trade-offs, and applying domain-informed heuristics. Unlike conventional optimization methods like Bayesian optimization that require predefined constraints, our approach uniquely combines autonomous constraint generation with interpretable, reasoning-guided parameter exploration. Reproducibility analysis across five independent trials demonstrates consistent convergence behavior, while model comparison reveals that reasoning-capable LLM architectures (o3, o1) are essential for successful optimization, with standard models failing to converge effectively. This approach shows significant potential for optimization scenarios where operational constraints are poorly characterized or unavailable, particularly for emerging processes and retrofit applications.