基于大语言模型智能体的化工过程PID整定的物理信息框架

中文摘要 AI 辅助

化工过程的PID整定通常依赖辨识得到的过程模型，而工厂工程师常通过观察响应、诊断缺陷、调整增益、验证结果的方式迭代整定回路。本研究将这种类工程师的工作流程形式化为语言模型辅助的PID整定框架，适用于大语言模型（LLMs）和小语言模型（SLMs）。托管式LLMs接收闭环响应特征、控制工程诊断、整定偏好以及基于内部模型控制（IMC）的演示，以在常见接受准则下生成并迭代修正PID增益。对于本地部署，通过带模拟验证IMC目标的监督微调（SFT）和带不可补偿稳定性与性能奖励的物理信息分组相对策略优化（PI-GRPO）对Qwen3-0.6B进行适配。在100个一阶加纯滞后（FOPDT）和100个二阶加纯滞后（SOPDT）测试案例中，托管式LLMs（DeepSeek-V4-Flash和Qwen3.7-Plus）的最终成功率分别达到75%-89%和77%-79%；对于Qwen3-0.6B，监督微调将首次推荐成功率提升至86.5%，PI-GRPO进一步将其提高到94.0%，主要改善了首次尝试的可靠性和稳定裕度。

英文摘要

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.

发表机构

• Zhejiang University（浙江大学）
• ZJU-Hangzhou Global Scientific and Technological Innovation Center（浙江大学杭州国际科创中心）
• Ningbo Innovation Center, Zhejiang University（浙江大学宁波科创中心）

机构由 AI 辅助整理，请以论文原文为准。