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This study introduces a language-model-assisted framework for PID tuning in chemical processes that mimics the iterative workflow of plant engineers. By utilizing closed-loop response features and control-engineering diagnostics, the framework enables both large and small language models to generate and refine PID gains effectively. Results show that fine-tuning the Qwen3-0.6B model significantly enhances first-recommendation success rates to 94.0%, demonstrating improved reliability and stability in tuning outcomes.
Fine-tuning a language model for PID tuning can boost first-attempt success rates to an impressive 94%, revolutionizing how chemical processes are optimized.
PID tuning for chemical processes commonly relies on identified process models, whereas plant engineers often retune loops iteratively by observing responses, diagnosing deficiencies, adjusting gains, and validating the result. This work formalizes this engineer-like workflow in a language-model-assisted PID tuning framework applicable to both large and small language models (LLMs/SLMs). Hosted LLMs receive closed-loop response features, control-engineering diagnoses, tuning preferences, and internal model control (IMC)-based demonstrations to generate and iteratively correct PID gains under common acceptance criteria. For local deployment, Qwen3-0.6B is adapted through supervised fine-tuning (SFT) with simulation-verified IMC targets and physics-informed group relative policy optimization (PI-GRPO) with non-compensable stability and performance rewards. On 100 first-order plus dead time (FOPDT) and 100 second-order plus dead time (SOPDT) test cases, hosted LLMs (DeepSeek-V4-Flash and Qwen3.7-Plus) achieve final success rates of 75-89% and 77-79%, respectively. As for Qwen3-0.6B, supervised fine-tuning raises first-recommendation success to 86.5%, and PI-GRPO further increases it to 94.0%, primarily improving first-attempt reliability and stability margins.
