Digital twin-driving force for petrochemical smart factory

doi:10.16085/j.issn.1000-6613.2023-0643

Abstract

Abstract:

The application of digital twin technology has played an increasingly important role in various fields, and its importance is also emphasized in the “14th Five-Year Plan for the Development of Intelligent Manufacturing” issued by the ministry of industry and information technology. This article describes the current application status of digital twin technology in the petroleum and petrochemical industry and designs a framework for the full lifecycle digital twin platform of petrochemical intelligent factories. Supported by industrial Internet platforms and focusing on the complex processes and devices of petrochemical enterprises, this framework can provide services such as digital delivery, intelligent construction, simulation of intelligent operations, and intelligent maintenance for petrochemical intelligent factories. In addition, three application scenarios are planned in this article, namely visual simulation for production scheduling in intelligent factories based on digital twins, intelligent inspection of factory equipment using augmented reality, and immersive training and safety drills for intelligent factories based on virtual reality. Finally, this article analyzes the challenges and prospects of applying digital twin technology in the petroleum and petrochemical industry, providing guidance and suggestions for its application in intelligent factories. The research findings of this article are of great significance in promoting the application of digital twin technology in the petroleum and petrochemical industry, and they also provide new ideas and methods for the application of digital twin technology in intelligent factories.

Key words: digital twin, smart plant 3.0, petroleum and petrochemical industry, life cycle

摘要：

数字孪生技术的应用已经在多个领域发挥着越来越重要的作用，我国工业和信息化部《“十四五”智能化制造发展规划》中也强调了数字孪生技术的重要性。本文阐述了数字孪生技术在石油石化行业的应用现状，并介绍了石化智能工厂全生命周期数字孪生平台框架。该框架以工业互联网平台为支撑，以石化企业复杂的工艺与装置为核心，可以提供石化智能工厂的数字化交付、智能建设服务、智能运行模拟以及智能维护等服务。同时，本文还规划了基于数字孪生的智能工厂生产调度可视仿真、基于增强现实的智能工厂设备智能巡检和基于虚拟现实的智能工厂沉浸式培训与安全演习三个应用场景。最后，本文分析了数字孪生技术在石油石化行业的应用挑战与措施，为数字孪生技术在智能工厂应用提供了指导建议。本文的研究成果对于推动数字孪生技术在石油石化行业的应用具有重要意义，同时也为数字孪生技术在智能工厂应用提供了新的思路和方法。

关键词: 数字孪生, 智能工厂3.0, 石油石化行业, 全生命周期

CLC Number:

SUO Hansheng, JIA Mengda, SONG Guang, LIU Dongqing. Digital twin-driving force for petrochemical smart factory[J]. Chemical Industry and Engineering Progress, 2023, 42(7): 3365-3373.

索寒生, 贾梦达, 宋光, 刘东庆. 数字孪生技术助力石化智能工厂[J]. 化工进展, 2023, 42(7): 3365-3373.

Figures/Tables 1

References 29

1	工业和信息化部, 国家发展和改革委员会, 教育部, 等. “十四五”智能制造发展规划[R/OL]. [2021-12-21]. .
2	GRIEVES Michael W. Product lifecycle management: The new paradigm for enterprises[J]. International Journal of Product Development, 2005, 2(1/2): 71.
3	PIASCIK B, VICKERS J, LOWRY D，et al. Technology area 12: Materials, structures，mechanical systems，and manufacturing road map[M]. Washington, DC: NASA Office of Chief Technologist, 2010: 3-35.
4	TUEGEL Eric J, INGRAFFEA Anthony R, EASON Thomas G, et al. Reengineering aircraft structural life prediction using a digital twin[J]. International Journal of Aerospace Engineering, 2011, 2011: 1-14.
5	EDWARD Glaessgen, DAVID Stargel. The digital twin paradigm for future NASA and U.S. air force vehicles[C]//53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. 23 April 2012—26 April 2012, Honolulu, Hawaii. Reston, Virginia: AIAA, 2012: 1818.
6	陶飞, 刘蔚然, 张萌, 等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统, 2019, 25(1): 1-18.
	TAO Fei, LIU Weiran, ZHANG Meng, et al. Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems, 2019, 25(1): 1-18.
7	中国电子技术标准化研究院, 树根互联技术有限公司. 数字孪生应用白皮书[R/OL]. [2020-11-11]. .
8	李剑峰. 工业元宇宙[M]. 北京: 电子工业出版社, 2022: 197-198.
	LI Jianfeng. Industrial metauniverse[M]. Beijing: Publishing House of Electronics Industry, 2022: 197-198.
9	任川, 陈磊. 建立在智能工厂基础上的企业数字孪生体[J]. 中国石油和化工标准与质量, 2018, 38(21): 127-128.
	REN Chuan, CHEN Lei. digital twin of enterprise based on smart factory[J]. China Petroleum and Chemical Standard and Quality, 2018, 38(21): 127-128.
10	李柏松, 王学力, 王巨洪. 数字孪生体及其在智慧管网应用的可行性[J]. 油气储运, 2018, 37(10): 1081-1087.
	LI Baisong, WANG Xueli, WANG Juhong. Digital twin and its application feasibility to intelligent pipeline networks[J]. Oil & Gas Storage and Transportation, 2018, 37(10): 1081-1087.
11	赵国深, 赵嘉玲, 刘思妤, 等. 数字孪生技术在油气管道管理中的应用[J]. 化工管理, 2022(4): 71-74.
	ZHAO Guoshen, ZHAO Jialing, LIU Siyu, et al. Application of digital twin technology in oil and gas pipeline management[J]. Chemical Management, 2022(4): 71-74.
12	陈斯迅, 李在蓉, 王禹钦, 等. 管道数字孪生体模型的构建及应用[J]. 油气储运, 2021, 40(6): 643-650.
	CHEN Sixun, LI Zairong, WANG Yuqin, et al. Construction and application of digital twin model of pipeline[J]. Oil & Gas Storage and Transportation, 2021, 40(6): 643-650.
13	宫敬, 徐波, 张微波. 中俄东线智能化工艺运行基础与实现的思考[J]. 油气储运, 2020, 39(2): 130-139.
	GONG Jing, XU Bo, ZHANG Weibo. Thinking on the basis and realization of intelligent process operation of China-Russia Eastern Gas Pipeline[J]. Oil & Gas Storage and Transportation, 2020, 39(2): 130-139.
14	熊明, 古丽, 吴志锋, 等. 在役油气管道数字孪生体的构建及应用[J]. 油气储运, 2019, 38(5): 503-509.
	XIONG Ming, GU Li, WU Zhifeng, et al. Construction and application of digital twin in the in-service oil and gas pipeline[J]. Oil & Gas Storage and Transportation, 2019, 38(5): 503-509.
15	杨剑锋, 杜金虎, 杨勇, 等. 油气行业数字化转型研究与实践[J]. 石油学报, 2021, 42(2): 248-258.
	YANG Jianfeng, DU Jinhu, YANG Yong, et al. Research and practice on digital transformation of the oil and gas industry[J]. Acta Petrolei Sinica, 2021, 42(2): 248-258.
16	王子宗, 刘洪谦, 王基铭. 甲醇制丙烯分离流程的研究与优化[J]. 化工学报, 2019, 70(1): 136-145.
	WANG Zizong, LIU Hongqian, WANG Jiming. Research and optimization of separation technology of methanol to propylene[J]. CIESC Journal, 2019, 70(1): 136-145.
17	叶磊, 徐慧, 王勇, 等. 基于数字孪生的前纺丝车间运行架构[J]. 制造业自动化, 2021, 43(6): 130-133.
	YE Lei, XU Hui, WANG Yong, et al. Operational architecture of pre-spinning workshop based on Digital Twin[J]. Manufacturing Automation, 2021, 43(6): 130-133.
18	陈晓红, 胡东滨, 曹文治, 等. 数字技术助推我国能源行业碳中和目标实现的路径探析[J]. 中国科学院院刊, 2021, 36(9): 1019-1029.
	CHEN Xiaohong, HU Dongbin, CAO Wenzhi, et al. Path of digital technology promoting realization of carbon neutrality goal in China’s energy industry[J]. Bulletin of Chinese Academy of Sciences, 2021, 36(9): 1019-1029.
19	蒋爱国, 王金江, 谷明, 等. 数字孪生驱动半潜式钻井平台智能技术应用[J]. 船海工程, 2019, 48(5): 49-52, 55.
	JIANG Aiguo, WANG Jinjiang, GU Ming, et al. Application of intelligent technology of semi-submersible drilling platform driven by digital twin[J]. Ship & Ocean Engineering, 2019, 48(5): 49-52, 55.
20	王文明, 侯春来, 武振宇, 等. 海洋无隔水管修井的数字孪生框架与可视化交互[J]. 计算机集成制造系统, 2021, 27(2): 423-431.
	WANG Wenming, HOU Chunlai, WU Zhenyu, et al. Frame and visualization for digital twin of marine riserless well intervention[J]. Computer Integrated Manufacturing Systems, 2021, 27(2): 423-431.
21	王金江, 王舒辉, 张来斌, 等. 基于数字孪生的压气站场设备风险智能决策系统[J]. 天然气工业, 2021, 41(7): 115-123.
	WANG Jinjiang, WANG Shuhui, ZHANG Laibin, et al. Digital twin based intelligent risk decision-making system of compressor station equipment[J]. Natural Gas Industry, 2021, 41(7): 115-123.
22	郭永峰. 美公司利用“数字孪生体”技术改进BHA钻具降低钻井成本[J]. 中国石油企业, 2021(S1): 74.
	GUO Yongfeng. american companies use “digital twin” technology to improve bha drilling tools and reduce drilling costs[J]. China Petroleum Enterprise, 2021(S1): 74.
23	黄珊, 陈健飞, 杨超, 等. 基于腐蚀大数据的管道数字孪生技术分析[J]. 中国石油和化工标准与质量, 2021, 41(3): 184-185, 188.
	HUANG Shan, CHEN Jianfei, YANG Chao, et al. Analysis of pipeline digital twinning technology based on corrosion big data[J]. China Petroleum and Chemical Standard and Quality, 2021, 41(3): 184-185, 188.
24	娄海川, 林雪茹, 刘凯. 用于石化装置的智能安全保障实时优化方法及系统: CN111949700A[P]. 2020-11-17.
	LOU Haichuan, LIN Xueru, LIU Kai. Intelligent safety guarantee real-time optimization method and system for petrochemical device: CN111949700A[P]. 2020-11-17..
25	王子宗, 高立兵, 索寒生. 未来石化智能工厂顶层设计：现状、对比及展望[J]. 化工进展, 2022, 41(7): 3387-3401.
	WANG Zizong, GAO Libing, SUO Hansheng. Designing petrochemical smart plant of the future: State of the art, comparison and prospects[J]. Chemical Industry and Engineering Progress, 2022, 41(7): 3387-3401.
26	张玉卓. 以数字化转型促进能源化工产业高质量发展[J]. 中国产经, 2021(2): 58-60.
	ZHANG Yuzhuo. Promoting the high-quality development of energy and chemical industry with digital transformation[J]. Chinese Industry & Economy, 2021(2): 58-60.
27	王子宗, 索寒生, 赵学良. 数字孪生智能乙烯工厂研究与构建[J]. 化工学报, 2023, 74(3): 1175-1186.
	WANG Zizong, SUO Hansheng, ZHAO Xueliang. Research and construction of digital twin intelligent ethylene plant[J]. CIESC Journal, 2023, 74(3): 1175-1186.
28	王子宗, 王基铭, 高立兵. 石化工业软件分类及自主工业软件成熟度分析[J]. 化工进展, 2021, 40(4): 1827-1836.
	WANG Zizong, WANG Jiming, GAO Libing. Classification and maturity analysis on petrochemical industrial software[J]. Chemical Industry and Engineering Progress, 2021, 40(4): 1827-1836.
29	陈岳飞, 肖珍芳, 方向. 数字孪生技术及其在石油化工行业的应用[J]. 天然气化工(C1化学与化工), 2021, 46(2): 25-30.
	CHEN Yuefei, XIAO Zhenfang, FANG Xiang. Digital twin technology and its application in petrochemical industry[J]. Natural Gas Chemical Industry, 2021, 46(2): 25-30.

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