发电技术 (Aug 2024)

Research on Predictive Maintenance Mode of Air Preheater Based on Digital Twin

  • LIU Wang,
  • CHEN Lian,
  • GONG Gaoyang,
  • LI Zhihua,
  • XUE Wenhua,
  • SHI Jingang,
  • XIE Jun,
  • LI Leilei,
  • YAO Rongcai,
  • WANG Zhaopeng,
  • YANG Yanxi,
  • DENG Yi,
  • ZHANG Chenhui

DOI
https://doi.org/10.12096/j.2096-4528.pgt.23054
Journal volume & issue
Vol. 45, no. 4
pp. 622 – 632

Abstract

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ObjectivesIn order to solve the shortcomings of the traditional preventive maintenance method of air preheater in large thermal power unit, a general mode of predictive maintenance based on digital twin was proposed, and the digital twin system of air preheater was constructed based on digital twin technology.MethodsThe proposed system included physical entity of rotary air preheater, real-time data acquisition and analysis module, digital twin model construction module, thermal parameter state monitoring, rotor thermal field video and thermal deformation visualization and ash accumulation prediction module. By the real-time acquisition of temperature parameter state and video data, and through the temperature field, video image, air leakage calculation and other modules, the calculation of thermal parameters and the prediction of ash accumulation were realized. At the same time, the 3D configuration screen was used to display data in real time, continuously optimize the accuracy of thermal parameter calculation and ash accumulation prediction, and realize the automatic optimization of soot-blowing strategy.ResultsThe proposed scheme realizes the state monitoring and dynamic control of the thermal calculation process of the air preheater, and solves the problem that the ash accumulation factor of the rotary air preheater in the existing power station affects the safe and reliable operation of the thermal power unit.ConclusionsThrough engineering testing of actual units, the proposed scheme effectively improves the operation and maintenance efficiency of the air preheater of thermal power units, verifies the feasibility of the proposed method, and provides technical support for the development of smart power plant systems in future.

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