物理先验指导的神经微分方程模型  

Physics-Informed Neural Differential Equation Model

作　　者：陈昊炜 郭宇 袁兆麟 王宝杰 班晓娟[1,4] CHEN Haowei;GUO Yu;YUAN Zhaolin;WANG Baojie;BAN Xiaojuan(School of Intelligence Science and Technology,University of Science and Technology Beijing,Beijing 100083,China;Shunde Innovation School,University of Science and Technology Beijing,Foshan 528300,China;Department of Industrial and Systems Engineering,Hong Kong Polytechnic University,Hong Kong 999077,China;Institute for Materials Intelligent Technology,Liaoning Academy of Materials,Shenyang 110004,China)

机构地区：[1]北京科技大学智能科学与技术学院,北京100083 [2]北京科技大学顺德创新学院,佛山528300 [3]香港理工大学工业与系统工程学系,香港999077 [4]辽宁材料实验室材料智能技术研究所,沈阳110004

出　　处：《北京邮电大学学报》2024年第4期90-97,共8页Journal of Beijing University of Posts and Telecommunications

基　　金：科技创新2030—重大项目(2022ZD0118001)。

摘　　要：流程工业中涉及多个复杂设备的耦合,独立设备模型无法有效指导实际生产;纯数据驱动模型常因面临分布外泛化问题,难以体现良好的数据效率和泛化能力。对此,针对浮选这一典型的流程工业系统,提出了一种物理先验指导的神经微分方程模型,该模型考虑设备间耦合关系和全局特征,利用物理先验对神经微分方程进行重构,以建模可感知环境的单智能体。所提模型由序列编码器、插值模块、神经微分方程预测模块和状态解码器构成,并基于物理先验设计了神经微分方程的梯度网络计算图结构。将多智能体模型按照实际工序拓扑建立不同体系,可以实现浮选全流程的长时液位预测,并作为在线仿真环境协助实现多智能体协同控制。使用从浮选厂采集的工业数据集对该模型进行了验证,结果表明,与离散时间模型和未借助物理信息重构梯度网络的基线模型相比,所提模型具有更优的数据效率和泛化能力。In process industries,the coupling of multiple complex devices makes it challenging for independent device models to effectively guide actual production.Pure data-driven models often face out-of-distribution generalization issues,resulting in poor data efficiency and generalization capabilities.In response to this,a physics-informed neural differential equation model is proposed for flotation,a typical process industry system.The model considers the coupling relationships between devices and global characteristics,utilizing physical priors to reconstruct neural differential equations to model an environment-aware single intelligent agent.The proposed model consists of a sequence encoder,an interpolation module,a neural differential equation inference module,and a state decoder.The gradient network computational graph structure of the neural differential equations is designed based on physical priors.By establishing different systems according to the actual process topology,the multi-agent model can achieve long-term liquid level prediction for the entire flotation process and assist in multi-agent collaborative control as an online simulation environment.The model was validated using an industrial dataset collected from a flotation plant.The results show that the proposed model demonstrates superior data efficiency and generalization capability compared with the discrete-time models and baseline models without leveraging physical information to reconstruct gradient network.

关 键 词：流程工业 体系化系统建模 神经常微分方程 理论引导的模型重构 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]