石油炼制与化工 ›› 2025, Vol. 56 ›› Issue (12): 91-96.

• 控制与优化 • 上一篇    下一篇

石化装置损伤识别数字化智能管理平台研究

田利1,宋耿良2,孙军鹏1,王盼1,王纪兵1,许可3,廖兵兵1   

  1. 1. 机械工业上海蓝亚石化设备检测所有限公司
    2. 上海市燃气管理事务中心
    3. 中石油云南石化有限公司
  • 收稿日期:2025-07-28 修回日期:2025-09-01 出版日期:2025-12-12 发布日期:2025-12-02
  • 通讯作者: 宋耿良 E-mail:2636273963@qq.com

RESEARCH ON DIGITAL INTELLIGENT MANAGEMENT PLATFORM FOR DAMAGE IDENTIFICATION OF PETROCHEMICAL PLANT

  • Received:2025-07-28 Revised:2025-09-01 Online:2025-12-12 Published:2025-12-02

摘要: 石化行业作为能源供应的关键环节,其装置的安全与稳定运行至关重要。针对传统石化装置损伤识别精度、风险预警时效性及决策科学性方面的不足,利用现代信息技术和智能模型建立了石化装置损伤识别数字化智能管理平台,并对其设计架构、功能界面、识别机理、描述模式进行了分析,进而对其企业实际应用效果进行了研究。分析结果表明:该平台架构框架包含4个层次,分别为展示层、业务逻辑层、数据层、服务支撑层;该平台实现了动态流程工厂可视化管理、损伤穿透管理、损伤机理和模式的标准化描述、知识库的动态更新和装置全生命周期数据驱动管控。实际应用效果表明,该平台的应用提高了石化装置损伤识别的准确性和时效性,实现了设备损伤预测和智能预警,为石化行业设备管理数字化转型提供了借鉴技术和智能管理框架。

关键词: 石化装置, 损伤识别, 预测预警, 智能管理平台

Abstract: As a key link in energy supply, the safety and stable operation of the petrochemical industry's facilities are of vital importance. In view of the deficiencies of traditional petrochemical plant damage identification accuracy, the timeliness of risk early warning and the scientific nature of decision-making, a digital intelligent management platform for petrochemical plant damage identification was established by using modern information technology and intelligent models. Its design architecture, functional interface, identification mechanism and description mode were analyzed, and then the actual application effect in enterprises was studied. The analysis results show that the platform architecture framework consists of four layers, namely,the display layer, business logic layer, data layer, and service support layer. This platform has achieved visual management of dynamic process factories, damage penetration management, standardized description of damage mechanisms and patterns, dynamic update of the knowledge base, and data-driven control throughout the entire life cycle of the equipment. The practical application effect shows that the application of this platform has improved the accuracy and timeliness of damage identification in petrochemical plants, achieved equipment damage prediction and intelligent early warning, and provided reference technology and intelligent management framework for the digital transformation of equipment management in the petrochemical industry.

Key words: petrochemical plant, damage identification, prediction and early warning, management platform