Neural network inverse control method for SCR denitration system of coal-fired unit

A neural network inverse technology for coal-fired units, applied in the field of neural network inverse control for SCR denitrification systems of coal-fired units, can solve problems such as lag, difficult control of SCR denitrification systems, and drastic changes in NOx concentration measurements

Pending Publication Date: 2019-06-25
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

The control of nitrogen oxides (NOx) emissions in coal-fired power plants mainly adopts Selective Catalytic Reduction (SCR) to implement flue gas denitrification. Due to the characteristics of nonlinearity, strong coupling, large delay, and lar

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  • Neural network inverse control method for SCR denitration system of coal-fired unit

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[0027] The technical solutions of the present invention will be further described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0028] There are three main aspects to realize the inverse control of the SCR denitrification system. One is the establishment of the mechanism model of the SCR denitrification system.

[0029] (1) Mechanism model of SCR denitrification system

[0030] This paper takes a 600MW unit as an example, the process flow chart of the SCR denitrification system is as follows figure 1 shown. The SCR denitration system is composed of liquid ammonia storage system, liquid ammonia evaporation system and SCR denitration reaction system. The SCR denitration reaction system generally adopts high temperature and high fly ash layout, that is, the SCR denitration reactor is installed between the economizer and the air preheater. The flue gas generated by the combustion of the boiler enters the denitrification system after ...

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Abstract

The invention discloses a neural network inverse control method for an SCR denitration system of a coal-fired unit. The method comprises the steps of establishment of an SCR denitration system mechanism model, training of an inverse model and application of a feedforward controller based on the inverse model; establishing a SCR denitration system mechanism model, including establishing a SCR denitration system model based on a Langmuir-Hinshelwod mechanism and an Eley-Rideal mechanism,; by analyzing field real data, a particle swarm or a genetic algorithm is used for optimizing and obtaining optimal parameters of a mechanism model, and the model can basically represent a real system; wherein the training of the inverse model comprises the steps of giving a group of random numbers to an input end based on a constructed accurate mechanism model of the SCR denitration system, setting simulation time as long as possible, and generating random output of the model; using the generated randomdata, and using an NARX neural network to train an inverse model; the application of the feedforward controller based on the inverse model comprises: the generated inverse model is combined with an original cascade PID control system of the coal-fired unit to form an SCR denitration inverse control system.

Description

technical field [0001] The invention belongs to the technical field of SCR denitrification control, and in particular relates to a neural network inverse control method for the SCR denitrification system of a coal-fired unit. Background technique [0002] With the improvement of national and people's awareness of environmental protection, the emission of nitrogen oxides (NOx) from coal-fired units in thermal power plants has attracted more and more attention. The control of nitrogen oxides (NOx) emissions in coal-fired power plants mainly adopts Selective Catalytic Reduction (SCR) to implement flue gas denitrification. Due to the characteristics of nonlinearity, strong coupling, large delay, and large inertia in the SCR denitrification system At the same time, the NOx concentration in the flue gas has the characteristics of drastic changes and measurement hysteresis, which makes the control of the SCR denitrification system very difficult. [0003] Based on intelligent cont...

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Application Information

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IPC IPC(8): G06F17/50G06N3/02G06N3/08
CPCY02P90/02
Inventor 赵文杰王朔王文广
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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