Method and device for optimizing boiler based on least square support vector machine (LSSVM) combustion model

Abstract

The invention provides a method for optimizing a boiler based on a least square support vector machine (LSSVM) combustion model. The method comprises the following steps of: acquiring an initial training sample set; acquiring LSSVM model parameters and a first characteristic matrix; acquiring a sample closest to a check sample from the training sample set as a reference sample, and performing row and column exchange on the first characteristic matrix to obtain a second characteristic matrix; calculating the inverse of the second characteristic matrix; acquiring partitioning parameters of the second characteristic matrix; acquiring a third characteristic matrix of an LSSVM model; calculating the inverse of the third characteristic matrix; acquiring the corrected LSSVM model parameters; assigning the third characteristic matrix to the first characteristic matrix, and assigning the inverse of the third characteristic matrix to the inverse of the first characteristic matrix; acquiring the combustion efficiency of the boiler by using the corrected LSSVM model; and performing combustion optimal control on a corrected combustion model. The invention also provides a device for optimizing the boiler based on the LSSVM combustion model. By the method and the device, the optimization difficulty of the combustion of the boiler in a power station can be reduced, and the real-time performance of optimization is improved.

Description

technical field [0001] The invention relates to the field of generator boiler performance control, in particular to a boiler optimization method and device based on a least square support vector machine combustion model. Background technique [0002] The research shows that the boiler has great time-varying characteristics, and the learning model of the combustion process will have large errors as time goes by, which will affect the performance of combustion optimization. [0003] Combustion optimization is an important means to improve the efficiency of power plant boilers and reduce pollutant emissions. However, the current combustion optimization has achieved multi-objective optimization of boiler efficiency and NOx emissions, and various learning algorithms are used to establish boiler efficiency and NOx emission models, and then the problem of multi-objective optimization of combustion is established, and genetic algorithms and particle swarm optimization are often used...

AI Technical Summary

Problems solved by technology

[0004] Due to the time-varying characteristics of boilers, with the passage of time and changes in boiler operating conditions, online calibration requires a long calculation time, which makes the process of obtaining manipulated variables complicated, and the real-time performance adjustment of power plant boilers is not high.

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