Thermal power plant boiler NOx emission modeling variable selection method

A variable selection and emission technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problem of obvious noise of related variables, the inability of the model to accurately describe the characteristics of the modeling object, and the inability to systematically describe and predict, etc. question
CN110263356APending Publication Date: 2019-09-20NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Patent Information

Authority / Receiving Office
CN ยท China
Current Assignee / Owner
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
Publication Date
2019-09-20

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Abstract

The invention discloses a thermal power plant boiler NOx emission modeling variable selection method. Whether the accuracy and the generalization ability of a model are affected or not is judged through variable selection. Aiming at the problem of variable selection, the maximum information coefficient variable selection method is provided by combining with the maximum information coefficient on the basis of the mutual information variable selection method, the maximum information coefficient is introduced to improve the original variable selection method, and the effectiveness of the method is verified by using the experimental data set of the nominal model.
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Description

technical field

[0001] The invention relates to the technical field of NOx emissions of thermal power plant boilers, in particular to a variable selection method for modeling NOx emissions of thermal power plant boilers. Background technique

[0002] The hysteresis of NOx measuring instruments in power plants leads to inaccurate SCR denitrification control, and it is necessary to model and predict boiler NOx emissions; the mathematical model of boiler NOx emissions is the basis for NOx emission optimization.

[0003] In the power industry, due to the complex on-site environment and many related variables, the noise is relatively obvious. If there is no effective method for variable selection, data modeling errors will occur, and the system cannot be accurately described and predicted. Whether the variable selection is accurate affects the accuracy and generalization ability of the model. Missing variables will cause the model to be unable to accurately describe the character...

Claims

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