Boiler combustion operation optimization method and system based on digital twin

Through digital twin technology, boiler data is collected in real time, combustion impact factors and efficiency factors are determined, and pollutant changes are predicted using integrated learning algorithms, which solves the problem of insufficient prediction capabilities of boiler combustion operation, and achieves efficient and clean combustion processes and accurate pollutant prediction.

CN118816182BActive Publication Date: 2025-03-18SHANDONG ENERGY GROUP LINGTAI THERMAL POWER GENERATION CO LTD
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Patent Information

Application Number
CN202410945684.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-07-15
Publication Date
2025-03-18
Estimated Expiration
2044-07-15

AI Technical Summary

Technical Problem

The existing boiler combustion operation prediction capabilities are weak, making it difficult to accurately predict performance changes, potential fault points and carbon emission fluctuations. The emission control strategy is complex, affecting the combustion process and pollutant emission forecasts.

Method used

The boiler combustion operation optimization method based on digital twins is adopted to collect the temperature, pressure and pollutant concentration data during boiler combustion in real time, determine the combustion impact factor and combustion efficiency factor, and use an integrated learning algorithm to predict the trend of pollutant concentration change, and feedback the boiler state to control the combustion temperature.

Benefits of technology

It improves the accuracy of pollutant emission forecasts, realizes precise control of boiler combustion temperature, ensures an efficient and clean combustion process, and reduces environmental impact.

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Abstract

The present application relates to the technical field of boiler combustion optimization, and specifically to a method and system for optimizing boiler combustion operation based on digital twins, the method comprising: real-time collection of temperature, pressure and pollutant concentration at each moment of boiler combustion; selecting multiple moments before each moment, recording them as historical moments of each moment; determining the combustion influencing factor at each moment; determining the combustion efficiency factor at each moment; based on the combustion efficiency factor, using an integrated learning algorithm for prediction, determining the trend of changes in the predicted concentration of pollutants, and the digital twin model feeds back the boiler state to the control system to control the boiler combustion temperature. The present application can improve the accuracy of the prediction of pollutant emission concentration, thereby controlling the boiler combustion temperature.
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