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

Establishing method and application of two-dimensional prediction model of silicon content in hot metal in blast furnace

A prediction model, blast furnace molten iron technology, applied in special data processing applications, instruments, electrical digital data processing, etc.

Active Publication Date: 2015-09-16
CENT SOUTH UNIV
View PDF4 Cites 22 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the prediction model of this method uses a multiple regression model, which cannot fit the nonlinear relationship between input variables and silicon content well, and the model does not have universal applicability

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
  • Establishing method and application of two-dimensional prediction model of silicon content in hot metal in blast furnace
  • Establishing method and application of two-dimensional prediction model of silicon content in hot metal in blast furnace
  • Establishing method and application of two-dimensional prediction model of silicon content in hot metal in blast furnace

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0121] This embodiment adopts a steel plant 2650m 3 Based on the actual production data of the blast furnace, a two-dimensional prediction experiment of the silicon content in molten iron was carried out.

[0122] A method for constructing a two-dimensional prediction model for the silicon content of molten iron in a blast furnace, specifically comprising the following steps:

[0123] Collect 850 sets of data of the blast furnace from January 9, 2013 to February 19, 2013. According to the method of step S1, through the correlation analysis between the various variables and the silicon content of molten iron, the forward selection method is used to determine the correlation with the silicon content of molten iron. A strong and appropriate number of input variable data sample sets; the variable selection results are shown in Table 3 below, and a total of 10 variables are used as input variables for the two-dimensional silicon content prediction model. Then, the Mahalanobis dist...

Embodiment 2

[0131] This embodiment relates to a two-dimensional prediction method for the silicon content of molten iron in blast furnace using the two-dimensional prediction model of silicon content in molten iron constructed in embodiment 1. Specifically, the sample set D in embodiment 1 is 3 Input the trained model as a test sample to get the point prediction value of silicon content prediction variance noise variance Then according to the formula (17), the final prediction interval is obtained; the prediction result is as follows Figure 4 As shown, the predicted value of the silicon content of the two-dimensional forecast model can well track the change of the measured value, especially in the case of large fluctuations in the actual value, the predicted value can basically maintain the same trend of change. Then, according to the formula (18), the credibility of the prediction results of different prediction interval width ranges is calculated, and the results are shown in Tabl...

Embodiment 3

[0136] This embodiment adopts a steel plant 2650m 3 Based on the actual production data of the blast furnace, a two-dimensional prediction experiment of the silicon content in molten iron was carried out.

[0137] A method for constructing a two-dimensional prediction model for the silicon content of molten iron in a blast furnace, specifically comprising the following steps:

[0138] Collect 1,150 sets of data of the blast furnace from March 2, 2015 to March 30, according to the method of step S1, through the analysis of the correlation between the various variables and the silicon content of molten iron, and use the forward selection method to determine the correlation with the silicon content of molten iron A strong and appropriate number of input variable data sample sets; the variable selection results are shown in Table 3 in Example 1, and a total of 10 variables are used as input variables for the two-dimensional silicon content prediction model. Then, the Mahalanobis ...

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 present invention relates to an establishing method of a two-dimensional prediction model of a silicon content in hot metal in a blast furnace. The method comprises: obtaining an input variable-data sample set; establishing the two-dimensional prediction model of the silicon content in the hot metal in the blast furnace based on a bootstrap prediction interval method. The present invention further relates to the application of the two-dimensional prediction model. The application comprises: outputting prediction results, namely the prediction value and the prediction interval of the silicon content, by utilizing the two-dimensional prediction model of the silicon content; calculating the relationship between the width of the prediction interval and the reliability of the prediction value by performing statistic analysis on the prediction results so as to finally obtain the two-dimensional prediction results of the silicon content in the hot metal. Through the method and application, disclosed by the present invention, the hit rate of the prediction of the silicon content value is increased, and besides the reliability of each prediction result of the silicon content is evaluated, so that operators can selectively compare the prediction results, and the capability of regulating and controlling the furnace temperature of the blast furnace is hopeful to be further raised.

Description

technical field [0001] The invention relates to a construction method and application of a two-dimensional prediction model of silicon content in molten iron during blast furnace smelting, and belongs to the technical field of automatic detection. Background technique [0002] Blast furnace temperature is an important parameter to measure the blast furnace condition, which is directly related to the smooth condition of the blast furnace. The extremely harsh environment inside the blast furnace makes it extremely difficult to control the furnace conditions. If there is a problem with the furnace temperature control, and the furnace temperature is "overheated" or "overcooled", it is easy to induce furnace condition failure. In actual production, since the temperature of the molten iron in the blast furnace cannot be directly measured, the silicon content of the molten iron is often used to indirectly characterize the furnace temperature. The silicon content of molten iron is...

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 Applications(China)
IPC IPC(8): G06F17/50
Inventor 蒋朝辉董梦林桂卫华阳春华谢永芳
Owner CENT SOUTH 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