Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

SCR flue gas denitration control method for coal-fired unit based on neural network predictive control

A technology of predictive control and coal-fired units, which is applied in the field of flue gas denitrification, can solve the problems of inability to accurately control the amount of ammonia injection in the SCR out-of-stock system, difficulty in ensuring NOx emission standards, and increase of ammonia escape, etc., to achieve easy online calculation and overcome waste reduction The effect of low requirements on agent and model

Inactive Publication Date: 2018-02-16
JILIN ELECTRIC POWER RES INST +3
View PDF4 Cites 35 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a coal-fired unit SCR flue gas denitrification control method based on neural network predictive control to solve the problem of difficulty in ensuring NOx emission standards and waste of reducing agents due to the inability to accurately control the ammonia injection volume of the SCR out-of-stock system under variable working conditions , increasing the problem of ammonia escape

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • SCR flue gas denitration control method for coal-fired unit based on neural network predictive control
  • SCR flue gas denitration control method for coal-fired unit based on neural network predictive control
  • SCR flue gas denitration control method for coal-fired unit based on neural network predictive control

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] Include the following steps:

[0037] Step S1, collect sample data about time changes of the SCR denitrification system, and determine the neurons of the input layer and output layer of the dynamic neural network according to the sample data;

[0038] In this embodiment, in order to provide the model network with input and output data pairs that comprehensively and correctly reflect system characteristics, the data collected and put into network training should satisfy the following three characteristics: ergodicity, compactness and compatibility. That is, the learning samples cover all possible state space conditions of the object as much as possible; and the learning sample density within a certain space range must be appropriate, so that the characteristics of the object can be collected. The number of samples required for training the network depends on the complexity of the research object and the influence of noise on the experimental object, that is, the system c...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to an SCR flue gas denitration control method for a coal-fired unit based on neural network predictive control and belongs to the technical field of flue gas denitration. The SCRflue gas denitration control method comprises the following steps: S1, acquiring sample data related to time variation of an SCR denitration system, and determining nerve cells of an input layer andan output layer of a dynamic neural network according to the sample data; S2, adopting the dynamic neutral network to perform model identification on the SCR denitration system and building an SCR prediction model; S3, utilizing the SCR prediction model to calculate a predicted value of the concentration of NOx at the outlet of the SCR denitration system, and utilizing the predicted value of the concentration of NOx at the outlet of the SCR denitration system to control the ammonia spray amount of the SCR denitration system. By use of the SCR flue gas denitration control method, the concentration of a nitric oxide at the outlet can be substantially kept unchanged, requirements can be accurately satisfied in real time on formulation of the ammonia spray amount, the problems of waste of a reductant and increase of ammonia escape are solved, and the accuracy in predicting and controlling the ammonia spray amount is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of flue gas denitrification, and relates to a control method for SCR flue gas denitrification of a coal-fired unit based on neural network predictive control. Background technique [0002] SCR (Selective Catalytic Reduction) - selective catalytic reduction is currently the most mature and widely used flue gas denitrification technology in the world. SCR is under the action of a catalyst, using the reducing agent NH 3 Wait to selectively react with NOx in the flue gas and generate non-toxic and non-polluting N 2 and H 2 O. The effect of SCR technology on the control of boiler flue gas NOx is very significant, and the technology is relatively mature. At present, it has become the most widely used and most effective flue gas denitrification technology in the world. [0003] In the denitrification system, the ammonia flow is multiplied by the NOx flow signal times the NH 3 / NOx molar ratio obtained, where, ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): B01D53/90B01D53/56G06N3/04
CPCB01D53/8625B01D53/8696B01D53/90B01D2251/2062G06N3/04
Inventor 徐博孟范伟夏志王松寒李航周宏伟都明亮崔希生王朔高长征史冬云朱爱军金春林马晓琴
Owner JILIN ELECTRIC POWER RES INST
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com