A boiler ammonia-doped combustion equipment operation and maintenance system

By constructing a digital twin model and using machine learning for fault prediction, the problem of incomplete equipment status detection in existing technologies has been solved, enabling intelligent operation and maintenance of ammonia-blended combustion equipment in pulverized coal boilers and improving the reliability and efficiency of equipment operation.

CN122390721APending Publication Date: 2026-07-14XI AN JIAOTONG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
XI AN JIAOTONG UNIV
Filing Date
2026-04-21
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

Existing technologies cannot fully and accurately reflect the overall operating status and potential failure risks of ammonia-blended combustion equipment in pulverized coal boilers. They lack the ability to predict equipment failures and provide intelligent operation and maintenance support, resulting in high equipment failure rates and significant safety hazards.

Method used

The data acquisition module monitors equipment operation data in real time, constructs a digital twin model for three-dimensional geometric modeling and mechanical heat transfer analysis, combines machine learning for fault prediction, and provides maintenance strategies and plans through an intelligent operation and maintenance decision support module.

Benefits of technology

It enables precise mapping of equipment operating status and intelligent operation and maintenance, improving operation and maintenance efficiency, reducing costs, and ensuring the efficient operation of equipment.

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Abstract

The application discloses a boiler ammonia blending combustion equipment operation and maintenance system, and relates to the technical field of energy and power engineering technology. The system comprises the following steps: by using three-dimensional modeling, finite element analysis and other technologies, a high-precision digital twin model is constructed for key equipment in the ammonia blending combustion system of a pulverized coal boiler, so that the physical characteristics and operating state of the equipment are accurately mapped; with the aid of a machine learning algorithm, historical and real-time data generated by the digital twin model are deeply analyzed, a fault prediction model is constructed, and potential equipment faults are identified in advance; combined with the fault prediction results and the real-time operating state of the equipment, an intelligent operation and maintenance decision support system is developed, maintenance strategy suggestions are provided, and a preventive maintenance plan is formulated. The application can effectively reduce the equipment failure rate, improve the overall reliability and operating efficiency of the system, solve the problem that faults in the operation and maintenance of existing pulverized coal boiler ammonia blending combustion equipment are difficult to be found and handled in time, and has important engineering application value.
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