A blast furnace molten iron quality monitoring method based on KPLS robustness reconstruction error

A technology for reconstructing errors and blast furnace molten iron, which is applied in the directions of instruments, adaptive control, control/regulation systems, etc., can solve problems such as difficulty in fault identification, and achieve the effect of ensuring the quality of molten iron

Active Publication Date: 2017-12-12
NORTHEASTERN UNIV
View PDF4 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problem that fault identification is difficult in the above KPLS-based blast furnace molten iron quality mo

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 blast furnace molten iron quality monitoring method based on KPLS robustness reconstruction error
  • A blast furnace molten iron quality monitoring method based on KPLS robustness reconstruction error
  • A blast furnace molten iron quality monitoring method based on KPLS robustness reconstruction error

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0053] The specific implementation manners of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0054] This embodiment provides a method for monitoring the quality of blast furnace molten iron based on KPLS (kernel projection to latent structures, kernel partial least squares) robust reconstruction error, including:

[0055] Step 1. Collect the blast furnace operating parameters and molten iron quality variables at the same time in the blast furnace ironmaking historical data, and use the blast furnace operating parameters as the input data matrix X, and the molten iron quality variables as the output data matrix Y:

[0056] The operating parameters of the blast furnace include variables measured by conventional detection instruments, variables adjusted at the upper and lower parts and variables obtained through calculation, including coke batch, ore batch, coke load, sintering ratio, cold air flow rate, air supply ratio, h...

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 provides a blast furnace molten iron quality monitoring method based on KPLS robustness reconstruction error. The method comprises the steps of collecting blast furnace operating parameters and molten iron quality variables at the same moment; selecting a training set and standardizing the same; mapping input data in the training set into a high-dimensional feature space to obtain a Gram matrix K and performing centralization processing; acquiring new blast furnace operating parameter and molten iron quality variable samples including abnormal working conditions as a testing set and performing standardization processing; mapping an input data matrix in the testing set in to the high-dimensional feature space to obtain a Gram matrix and performing centralization processing; building a partial least square model to describe the high-dimensional feature space and an output data matrix; checking whether a blast furnace iron making process is abnormal by using a T2 statistical quality and a Q statistical quality; calculating a reconstruction value of original process variable data and identifying process variables causing abnormal working conditions of the blast furnace. The method can accurately identify faults in blast furnace molten iron quality monitoring and improve molten iron quality monitoring performance, thus guaranteeing blast furnace molten iron quality.

Description

technical field [0001] The invention belongs to the technical field of blast furnace molten iron quality monitoring, in particular to a blast furnace molten iron quality monitoring method based on KPLS robust reconstruction error. Background technique [0002] Blast furnace ironmaking is an important link in steel production and the main method of modern ironmaking. Due to the good technical and economic indicators of blast furnace ironmaking, simple process, large production volume, high productivity and low energy consumption, the iron produced by this method accounts for more than 95% of the world's total iron. Blast furnace ironmaking is the reduction of iron from iron ore and melting it into pig iron. Blast furnace ironmaking is a continuous production process, and the whole process is completed in the mutual contact process of furnace charge from top to bottom and gas from bottom to top. During the operation of the two major streams of the blast furnace, complex chem...

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): G05B13/04
CPCG05B13/042
Inventor 周平梁梦圆荣键刘记平柴天佑
Owner NORTHEASTERN UNIV
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