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

A large-scale coal-fired power plant co 2 Capture system and feed-forward control method

A neural network inverse, coal-fired power station technology, applied in the field of CO2 capture system and feedforward control of large coal-fired power stations, can solve the problems of measurement noise controller interference, difficulty in obtaining control quality, large inertia and delay, etc. Ensure control quality and improve the effect of dynamic adjustment quality

Active Publication Date: 2021-07-09
SOUTHEAST UNIV
View PDF8 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At the same time, studies have shown that the large-scale coal-fired power plant capture system has large inertia and delay, and the presence of disturbance, measurement noise, and uncertainty will also interfere with the controller to a certain extent, making it difficult to obtain good control quality
Currently for large coal-fired power plants CO 2 The capture system usually adopts the conventional PID control scheme, which is difficult to effectively deal with the large delay and strong coupling characteristics of the controlled object

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
  • A large-scale coal-fired power plant co <sub>2</sub> Capture system and feed-forward control method
  • A large-scale coal-fired power plant co <sub>2</sub> Capture system and feed-forward control method
  • A large-scale coal-fired power plant co <sub>2</sub> Capture system and feed-forward control method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0049] (1) Determine the CO of large coal-fired power plants 2 The capture system control loop and the corresponding control and controlled quantities are shown in Table 1:

[0050] Table 1

[0051]

[0052]

[0053] (2) Set sampling time T=30s, use controlled quantity data as neural network input, controller data as neural network data, use BP neural network toolbox to establish coal-fired power station CO 2 Capture system inverse model. The neural network contains two hidden layers, the number of neurons is 20 and 5 respectively, and the training function is traindm;

[0054] (3) According to the given value r(k+1) and the past input data u(k-1) and output data y(k), calculate the output u of the neural network inverse controller NN (k);

[0055] (4) Set the relevant parameters of the PID control compensator, as shown in formula (4):

[0056]

[0057] (5) Calculate the deviation. e(k)=r(k+1)-y(k+1);

[0058] (6) Calculate the PID control compensation output a...

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 discloses a large-scale coal-fired power plant CO 2 Capture system and feed-forward control method to reduce CO in coal-fired power stations 2 The capture system is regarded as a five-input-five-output multivariable system, and the main steam pressure, enthalpy value at the outlet of the steam-water separator, unit power generation, CO 2 The capture rate and reboiler temperature are the main controlled variables, and the unit coal feed, water feed, main steam valve, lean liquid flow and reboiler steam flow are selected as the corresponding control variables. The present invention adopts BP neural network technology to establish a large-scale coal-fired power station CO 2 Capture the inverse model of the system, so that the required control variables can be calculated according to the given value, and advance control can be realized, which can effectively deal with the large delay characteristics of the overall system and improve the dynamic adjustment quality of the output side; in addition, by adding a PID control compensator Realize the correction of the inverse model of the neural network, thereby enhancing its anti-disturbance and uncertainty capabilities, and making the control system adapt to the needs of industrial sites.

Description

technical field [0001] The invention relates to the field of thermal automatic control, in particular to a large-scale coal-fired power plant CO 2 Capture system and feedforward control method. Background technique [0002] Thermal power units are the current CO 2 The most important source of emissions of gases has a great impact on the greenhouse effect. Post-combustion CO based on chemisorption 2 capture technology is to achieve CO 2 An important measure to capture and reduce greenhouse gas emissions. Post-combustion CO with MEA as adsorption solvent 2 Capture technology, with its advantages of high efficiency, high economy, mature technology and easy adjustment, has become the current commercial CO2 capture technology in the world. 2 Mainstream capture technology; meanwhile, post-combustion CO 2 The capture technology does not need to change the operating structure of the existing thermal power unit, and can be effectively operated by adding capture equipment after...

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
Patent Type & Authority Patents(China)
IPC IPC(8): G05B13/02B01D53/78B01D53/62
CPCB01D53/62B01D53/78B01D2257/504Y02A50/20Y02C20/40
Inventor 吴啸廖霈之李益国沈炯
Owner SOUTHEAST UNIV
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