Development and prospect of digital twin technology in vanadium-titanium ore-based iron and steel production process
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摘要: 钒钛磁铁矿作为战略性资源，其高效冶炼对我国钢铁工业至关重要。钒钛磁铁矿冶炼过程中面临矿中钛元素回收率低、工艺流程智能化程度欠缺、高炉冶炼技术优化难度高、综合能源智慧管理欠缺等问题，影响其产品升级和产能提高。数字孪生技术通过构建虚实融合的智能系统，可助力实现钒钛矿钢铁生产全流程的工艺优化、设备研发和智能控制。目前，相关研究尚处于探索阶段，研究成果较少且缺乏系统性。为此，介绍了数字孪生的内涵与发展历史，系统梳理了数字孪生在钒钛矿钢铁生产流程中的研究热点，总结了相关研究结果与工程实践，展望了数字孪生技术未来的发展趋势，为后续研究人员提供研究思路，以促进数字孪生技术应用，提升我国特色钒钛资源利用与钢铁智能制造水平。Abstract: Vanadium-titanium magnetite, as a strategic resource, its efficient smelting is of vital importance to China's steel industry. During the smelting process of vanadium-titanium magnetite, problems such as low recovery rate of titanium in the ore, insufficient intelligence of the process flow, high difficulty in optimizing blast furnace smelting technology, and lack of comprehensive energy intelligent management are faced, which affect its product upgrading and capacity improvement. Digital twin technology can help achieve process optimization, equipment research and development, and intelligent control throughout the entire production process of vanadium-titanium ore steel by building an intelligent system that integrates the virtual and the real. At present, the relevant research is still in the exploratory stage, with few research achievements and a lack of systematicness. For this purpose, the connotation and development history of digital twins were introduced. The research hotspots of digital twins in the production process of vanadium-titanium ore steel were systematically sorted out. The relevant research results and engineering practices were summarized, and the future development trends of digital twin technology were prospected, providing research ideas for subsequent researchers to promote the application of digital twin technology and enhance the utilization of characteristic vanadium-titanium resources and the intelligent manufacturing level of steel in China.
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图 4 钒钛磁铁矿高炉法冶炼流程
Figure 4. The blast furnace smelting process of vanadium-titanium magnetite
表 1 钢厂多源异构数据来源对比
Table 1. Comparison of multi-source heterogeneous data sources in steel mills
Feature dimension PLC data DCS Data SCADA Data Others Data sources Production-line controllers Distributed control systems Supervisory layer MES/ERP, lab testing Data types Boolean, integer Float, analog Mixed types Structured tables,
unstructured imagesData frequency Milliseconds Seconds-minutes Seconds-hours Minutes-days Typical uses Equipment interlocking
& fault diagnosisProcess parameter optimization Plant-wide visualization,
alarming, reportingQuality traceability
& schedulingData storage Local cache
(circular buffer)Real-time database Relational database Data warehouse/hadoop Communication protocols Modbus, profibus OPC UA, foundation fieldbus OPC DA, TCP/IP HTTP/REST, MQTT Heterogeneity challenges Inconsistent protocols Large volume & time-
series processingMulti-system interface
compatibilityDifficult fusion of structured
& unstructured dataApplication examples
in steel industryRolling-mill E-stop
signals, motor speedDynamic adjustment of BF
hot-metal compositionEnergy dashboard, overall
equipment effectivenessSteel surface-defect
image recognition
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