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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信息物理系统(Cyber-physical system, 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.
Abstract:Process industry is a part of manufacturing industry and foundation of national economic development, mainly including chemical industry, metallurgy, petrochemical and other industries. Its safe and efficient production has strategic importance to the country. However, process industry is facing the problems of low efficiency of resource utilization, poor production quality and serious environmental pollution. With the development of information technology and artificial intelligence, the technology needed to create digital twin for process industry is gradually mature. The digital twin for process industry with the combination of virtual and real, intelligent and optimized operation, has a great application value and potential to change the whole industry. This paper focuses on the digital twin technology of process industry. First, we expound the necessity and importance of the application of digital twin and describe the mapping relationship between the abstract model of process industry and the digital twin theoretical model. Secondly, we summarize the concept of digital twin, and compare it with CPS (cyber-physical system), industrial internet and other concepts. Then, according to the sequence of data flow of digital twin system, this paper puts forward six key technologies of digital twin system, including data intelligent perception, multi-source heterogeneous data integration, data efficient transmission, digital twin construction, enhanced interaction and transformation application. Finally, taking a digital twin production line of ironmaking system as an example, the solution of digital twin system in practical industry is shown.
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Key words:
- Digital twin /
- process industry /
- key technology /
- practical application
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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 
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表 2 三种建模方法对比
Table 2 Comparison of three modeling methods
建模方法 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
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表 3 3R技术对比
Table 3 Comparison of 3R technology
3R技术 真实场景信息 交互性 实时性 场景注册跟踪 相关场景 增强现实 包含 与真实环境交互 低 需要 工厂智能头盔 虚拟现实 不包含 与虚拟环境交互 较高 不需要 VR 数字化学习工厂 混合现实 包含 同时与真实环境和虚拟环境交互 高 不需要 设备维修 MR 辅助指导技术
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