Method for controlling flue gas desulfurization based on neural network prediction

A predictive control and neural network technology, applied in general control systems, control/regulation systems, instruments, etc., can solve problems such as difficulty in controlling slurry flow in spray towers, ensuring flue gas emission standards, and system nonlinearity, etc. problem, to achieve the effect of convenient online calculation, reliable design principle and strong nonlinearity

Inactive Publication Date: 2019-02-15
QILU UNIV OF TECH
View PDF8 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When the load of the unit is in a steady state, a better control effect can be obtained, but under the condition of variable attack, the system presents nonlinearity and large hysteresis. At this time, it is difficult to better control the flo

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
  • Method for controlling flue gas desulfurization based on neural network prediction
  • Method for controlling flue gas desulfurization based on neural network prediction
  • Method for controlling flue gas desulfurization based on neural network prediction

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0031] The present invention will be described in detail below with reference to the drawings and through specific embodiments. The following embodiments are for explaining the present invention, and the present invention is not limited to the following embodiments.

[0032] Such as figure 2 As shown, a method for predictive control of flue gas desulfurization based on neural network provided by the present invention is characterized in that it comprises the following steps:

[0033] Step S1, collecting sample data about time changes in the wet flue gas desulfurization system, and determining the neurons of the input layer and output layer of the dynamic neural network according to the collected sample data;

[0034] In this embodiment, in order to provide the neural network prediction model with input and output data that are correct and can completely reflect the characteristics of the system, the collected and applied network training data should have the characteristics of ergodi...

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 a method for controlling flue gas desulfurization based on neural network prediction. The method is characterized by comprising the following steps: S1, collecting sample datarelated to time variation in a wet flue gas desulfurization system, and determining neurons of an input layer and an output layer of a dynamic neural network according to the collected sample data; S2, modeling the wet flue gas desulfurization system by using the dynamic neural network in the S1 to establish a prediction model of the wet flue gas desulfurization system; and S3, calculating a sulfur dioxide concentration prediction value at a flue gas outlet of the wet flue gas desulfurization system by using the prediction model of the wet flue gas desulfurization system established in the S2, and controlling a slurry spray amount of the wet flue gas desulfurization system by using the sulfur dioxide concentration prediction value.

Description

Technical field [0001] The invention belongs to the technical field of flue gas desulfurization, and specifically relates to a method for predicting and controlling flue gas desulfurization based on neural network. Background technique [0002] The limestone-gypsum wet flue gas desulfurization technology uses limestone powder and water to make a slurry as an absorbent, pumped into the absorption tower and fully contacted and mixed with the flue gas. The sulfur dioxide in the flue gas and the calcium carbonate in the slurry and the air blown in from the lower part of the tower The oxidation reaction generates calcium sulfate. After calcium sulfate reaches a certain degree of saturation, it crystallizes to form dihydrate gypsum. [0003] The gypsum slurry discharged from the absorption tower is concentrated and dehydrated to make its water content less than 10%, and then sent to the gypsum storage bin by a conveyor for stacking. The flue gas after desulfurization is passed through a ...

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): G05B17/00
CPCG05B17/00
Inventor 马凤英于文志孙凯吴修粮
Owner QILU UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap