Digital twin system for predictive maintenance of green aviation kerosene Fischer-Tropsch reactor
Through the digital twin system combining mechanism and data-driven model, potential faults of Fischer-Tropsch reactors are identified and maintenance strategies are generated, which solves the problem of difficulty in timely discovering faults in traditional maintenance methods, and achieves the effect of improving equipment efficiency and safety.
CN119624421APending Publication Date: 2025-03-14CHONGQING LANZE ENERGY TECH CO LTD
Patent Information
- Application Number
- CN202411693505.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-25
- Publication Date
- 2025-03-14
AI Technical Summary
Technical Problem
The traditional Fischer-Tropsch reactor maintenance method is difficult to detect potential failures in a timely manner, resulting in possible safety hazards and inefficiency of equipment.
Method used
The digital twin system is adopted to select the best response points and predict the reaction trend through the combination of mechanism models and data-driven models, identify potential failures, and generate predictive maintenance strategies.
Benefits of technology
Significantly reduce unplanned downtime, improve equipment utilization and safety, optimize reaction conditions, reduce energy consumption and emissions, and improve economic benefits.
✦ Generated by Eureka AI based on patent content.
Smart Images
Figure CN119624421A_ABST
Abstract
The invention discloses a digital twin system for predictive maintenance of a green aviation kerosene Fischer-Tropsch reactor, and the system comprises a data processing module which is used for collecting and integrating temperature data and reaction state data of a plurality of sections of reactors; the digital twinning module is used for constructing a Fischer-Tropsch reactor mechanism digital twinning model to simulate real equipment behaviors; the trend prediction module is used for analyzing the acquired temperature data and the reaction state data of the multi-section reactor and driving a digital twin model to predict the trend of the reactor parameters and the reaction state data; and the maintenance decision module is used for generating a predictive maintenance strategy for the Fischer-Tropsch reactor according to the prediction result of the reaction state data trend. According to the method, the mechanism model and the data driving model are combined, the reaction optimal point is selected, the reaction trend is predicted, potential faults are identified, and efficiency and safety are improved.
Need to check novelty before this filing date? Find Prior Art
Citation Information
Cited By
Reaction condition optimization control method and system for biological aviation kerosene production
CN120215283A
Method for simulating storage stability of ethylene carbonate based on digital twinning
CN121905317A