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Combustion optimization control method for boiler

A boiler combustion and optimization control technology, applied in the optimal operation of power plant boiler combustion system, in the field of power plant boiler combustion optimization control of online incremental learning fuzzy neural network, can solve problems such as solving difficult problems of nonlinear rolling optimization

Inactive Publication Date: 2015-07-15
SOUTHEAST UNIV
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Problems solved by technology

Among them, the solution of nonlinear rolling optimization is difficult to find, and generally only numerical optimization can be used to solve it.

Method used

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  • Combustion optimization control method for boiler
  • Combustion optimization control method for boiler
  • Combustion optimization control method for boiler

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Embodiment Construction

[0041] Below in conjunction with accompanying drawing, further illustrate the present invention, it should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand the various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of this application.

[0042] The invention provides an online incremental learning fuzzy neural network power plant boiler combustion optimization control method. By sampling the boiler combustion nonlinear system in real time, the online incremental learning fuzzy neural network is used to train the real-time sampling data to establish boiler combustion. The data-driven model is optimized, and the boiler combustion optimization model is optimized and controlled using a nonlinear model predictive control (MPC) algorith...

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Abstract

The invention discloses a combustion optimization control method for a boiler. The combustion optimization control method is characterized by comprising the following steps: sampling a combustion nonlinear system of the boiler to obtain input / output data at the current moment; training the real-time sampled input / output data by an online incremental learning fuzzy neural network, building an online incremental learning predicting model of the combustion nonlinear system of the boiler; performing a nonlinear prediction control algorithm on the online incremental learning predicting model for realizing the optimization and the control of the combustion process of the boiler. According to the combustion optimization control method for the power station boiler of the online incremental learning fuzzy neural network, the nonlinear optimization problem in the predication control algorithm is solved by utilizing a particle swarm optimization algorithm through the online identification of the boiler combustion optimization model; the real-time optimization and control of the boiler combustion process are realized.

Description

technical field [0001] The invention relates to an optimization operation technology of a power plant boiler combustion system, in particular to a power plant boiler combustion optimization control method based on an online incremental learning fuzzy neural network, which belongs to the technical field of thermal automatic control. Background technique [0002] Combustion optimization is an important means to improve the efficiency of power plant boilers and reduce pollutant emissions. The current combustion optimization technology mostly uses learning algorithms such as neural network or support vector machine to establish boiler combustion efficiency and NO x The emission model uses intelligent search algorithms such as genetic algorithm to optimize the combustion optimization target value, and obtains the control variables for boiler combustion optimization. [0003] Research shows that boilers have great time-varying characteristics. With the passage of time and changes...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): F23N5/00
Inventor 林祥刘西陲吴啸李益国沈炯潘蕾
Owner SOUTHEAST UNIV
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