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流程工业数字孪生关键技术探讨

李彦瑞 杨春节 张瀚文 李俊方

李彦瑞, 杨春节, 张瀚文, 李俊方. 流程工业数字孪生关键技术探讨. 自动化学报, 2020, 45(x): 1−14 doi: 10.16383/j.aas.c200147
引用本文: 李彦瑞, 杨春节, 张瀚文, 李俊方. 流程工业数字孪生关键技术探讨. 自动化学报, 2020, 45(x): 1−14 doi: 10.16383/j.aas.c200147
Li Yan-Rui, Yang Chun-Jie, Zhang Han-Wen, Li Jun-Fang. Discussion on key technologies of digital twin in process industry. Acta Automatica Sinica, 2020, 45(x): 1−14 doi: 10.16383/j.aas.c200147
Citation: Li Yan-Rui, Yang Chun-Jie, Zhang Han-Wen, Li Jun-Fang. Discussion on key technologies of digital twin in process industry. Acta Automatica Sinica, 2020, 45(x): 1−14 doi: 10.16383/j.aas.c200147

流程工业数字孪生关键技术探讨

doi: 10.16383/j.aas.c200147
基金项目: 国家自然科学基金(61933015), 国家自然科学基金(61903326)资助
详细信息
    作者简介:

    李彦瑞:浙江大学控制与工程学院硕士研究生. 主要研究方向为时间序列分析, 工业大数据. E-mail: liyanrui@zju.edu.cn

    杨春节:浙江大学控制与工程学院教授. 主要研究方向为高炉故障诊断, 工业互联网, 数字孪生. E-mail: cjyang999@zju.edu.cn

    张瀚文:浙江大学控制与工程学院博士后. 主要研究方向为工业过程监测、设备剩余使用寿命预测、流程行业数字孪生. E-mail: zhanghanwen@zju.edu.cn

    李俊方:浙江大学控制与工程学院博士研究生. 主要研究方向为时间序列建模, 工业过程建模优化. E-mail: yooo-li@zju.edu.cn

Discussion on Key Technologies of Digital Twin in Process Industry

Funds: National Natural Science Foundation of China (61933015), National Natural Science Foundation of China (61903326)
  • 摘要: 流程工业是制造业的重要组成部分, 是国民经济发展的重要基础, 主要包括化工、冶金、石化等行业, 其安全高效的生产对国家而言具有重要的战略意义. 然而, 流程工业物理化学变化反应复杂、流程间能质流严重耦合、多目标冲突、在线实验风险大, 给生产流程系统建模与高效协同优化带来极大困难, 严重制约了生产质量和资源利用率的进一步提升. 随着信息技术与人工智能的发展, 建立虚实结合、协同优化运行的流程工业数字孪生生产线所需技术逐渐成熟, 其在流程工业的应用价值与潜力日益凸显. 本文首先阐述数字孪生在流程工业应用的必要性与重要性, 并通过边界定义法将数字孪生与CPS、工业互联网等概念进行对比分析,从而明确数字孪生的基本内涵与功能边界. 其次描述流程工业抽象模型和数字孪生理论模型间的映射关系, 并分析了如何用数字孪生技术解决流程工业系统建模与高效协同优化的瓶颈问题. 最后, 从数字孪生系统构建的角度探讨数字孪生发展的关键技术, 并以一条炼铁生产线为例, 展示数字孪生技术在实际工业中的应用解决方案.
  • 图  1  流程工业抽象结构

    Fig.  1  Abstract structure of process industry

    图  2  流程工业树结构

    Fig.  2  Process industry tree structure

    图  3  quad 理想数字孪生体的协同优化

    Fig.  3  Collaborative optimization of ideal digital twins

    图  5  虚实交互数字孪生体

    Fig.  5  Virtual reality interactive digital twin

    图  6  数字孪生关键技术

    Fig.  6  Key technologies of digital twin

    图  4  数字孪生体的反馈优化

    Fig.  4  Feedback optimization of digital twins

    图  7  炼铁生产线数字孪生系统技术架构图

    Fig.  7  Technical architecture of ironmaking digital twin system

    表  1  多采样率时序数据处理方法

    Table  1  Multi rate time series data processing method

    多采样率时序数据处理方法 优点 缺点
    单维估计法 统计法 简单, 填补速度快 填补结果有偏 忽略多维度间影响
    插值法 保留属性趋势 对采样频率有要求
    时间序列法 保留数据时序特点 难以运用在非稳定序列
    降采样 简单方便 丢失大量信息
    多维估计法 考虑多维数据间的影响 计算量大, 容易过拟合
    下载: 导出CSV

    表  2  三种建模方法对比

    Table  2  Comparison of three modeling methods

    优点 缺点
    基于知识建模 模型简单、对极端情
    况建模效果好
    模型精度低、无法实时更新、
    对不同场景无法迁移、
    建立专家知识库人力成本大
    基于机理建模 模型覆盖变量空间大、
    模型可脱离物理实体、
    模型具有可解释性
    计算复杂、难以对耦合复杂
    的流程工业建模
    基于数据建模 模型精度高、可动态更新 需要数据支持、工业过程
    数据集分布不均匀,
    异常情况数据少、
    模型没有可解释性
    下载: 导出CSV

    表  3  3R技术对比

    Table  3  Comparison of 3R technology

    增强现实 虚拟现实 混合现实
    真实场
    景信息
    包含 不包含 包含
    交互性 与真实环
    境交互
    与虚拟环境交互 同时与真实环境和
    虚拟环境交互
    实时性 较高
    场景注
    册跟踪
    需要 不需要 不需要
    相关场景 工厂智能头盔 VR数字化学习工厂 设备维修MR辅助
    指导技术
    下载: 导出CSV
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  • 收稿日期:  2020-03-20
  • 录用日期:  2020-09-07

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