Power station boiler emission soft measuring method based on least squares support vector machine and on-line updating

Abstract

The invention provides a power station boiler emission soft measuring method based on a least squares support vector machine and on-line updating and belongs to the thermal technology and artificial intelligence cross technology field. According to the power station boiler emission soft measuring method based on the least squares support vector machine and the on-line updating, relevant operation and state parameters of the power station boiler are selected to be served as model input, to-be-premeasured emission component contents are served as model output, historical operation data is selected to serve as initial training samples, an initial model for emission discharging is constructed through a least squares support vector machine method; updating strategies based on sample replacement and sample supplement based on the analysis of time-variant characteristics of emission discharging are also provided, and parameter solving and model updating are achieved in an incremental mode through two modes of deleting samples and increasing samples. The power station boiler emission soft measuring method based on the least squares support vector machine and the on-line updating has the advantages of improving model performance along with variation of process characteristics self-adaptively, achieving accurate prediction of the emission discharging and having significance to safe and optimizing operation of the power station boiler.

Description

technical field [0001] The invention relates to a soft measurement method for flue gas of a power plant boiler based on a least squares support vector machine (LSSVM) and online update, and belongs to the cross-technical field of thermal technology and artificial intelligence. Background technique [0002] In order to ensure the safety and optimal operation of power plant boilers, it is often necessary to obtain relevant information on parameters such as carbon content in fly ash and NOx emissions in the flue gas at the tail of the boiler. At present, these parameters are often measured by hardware sensors such as fly ash carbon meter and flue gas continuous monitoring system (continuous emission monitoring system, CEMS), but the installation and maintenance costs of these instruments are high, and because they work in harsh electromagnetic environments In , offline maintenance is often required. Therefore, it is of great engineering significance to use other easily measura...

AI Technical Summary

Problems solved by technology

However, in fact, most of the data stored in the database are normal operating conditions, and there is no active adjustment and setting of various thermal parameters, so it is difficult to ensure that the selected samples can cover all operating conditions.

After the model is established, changes in operating instructions and adjustment parameters during operation may bring about new working conditions, and the model will not be able to accurately predict the content of flue gas components

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