Researchers have developed a novel workflow that leverages large language models (LLMs) to automate the generation and tuning of process control strategies. This system breaks down the complex design process into sequential, code-generation steps, including plant-interface construction, controller specification, and simulation. The workflow was successfully demonstrated on a nonlinear gas-preheater benchmark, producing a functional control structure and an executable tuning environment. Automated tuning via Bayesian optimization significantly improved performance, reducing errors by approximately 26.5% compared to the initial LLM-generated controller. AI
IMPACT Automates complex engineering tasks, potentially accelerating industrial process optimization and control system design.
RANK_REASON The cluster contains a research paper detailing a novel methodology. [lever_c_demoted from research: ic=1 ai=1.0]
- Bayesian optimization
- Large Language Models
- PI (proportional-integral) feedback-feedforward control structure
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