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

Molten iron automatic slagging-off control method based on deep learning

A deep learning and control method technology, applied in the field of hot metal desulfurization and slag removal, can solve problems such as waste of hot metal and affecting the quality of molten steel

Active Publication Date: 2020-02-11
WISDRI WUHAN AUTOMATION
View PDF5 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The core technology of molten iron slag removal is liquid level image processing and slag volume decision-making. Deep removal will bring out a large amount of molten iron, resulting in waste of molten iron. Less removal will cause sulfur return during converter steelmaking and affect the quality of molten steel.

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
  • Molten iron automatic slagging-off control method based on deep learning
  • Molten iron automatic slagging-off control method based on deep learning
  • Molten iron automatic slagging-off control method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0128] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0129] Combine below Figure 1 to Figure 7 The specific embodiment of the present invention is introduced as a deep learning-based automatic slag removal control method for molten iron, which specifically includes the following steps:

[0130] Step 1: Perform feature extraction on the collected ladle liquid surface image to construct the training set and test set of the deep convolutional neural network, and obtain the slag level through manual labeling;

[0...

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 molten iron automatic slagging-off control method based on deep learning. The method comprises the following steps: performing feature extraction on a collected ladle liquid level image, and manually labeling to obtain a slag quantity grade for constructing a data set of a deep convolutional neural network; establishing a deep convolutional neural network framework, and performing training optimization on the deep convolutional neural network model by using an Adam algorithm to obtain an optimized network model; constructing factor types through factors influencing molten iron desulphurization slag, selecting the factor types to which the slag quantity grade standard belongs through a k-nearest neighbor method, and obtaining deep convolutional neural network modelsof different factor types according to the steps 1 to 3; and determining the overall slag quantity grade according to the corresponding network model output layer data, and making a corresponding slagging-off action through system judgment according to the model output. According to the method, the on-site image information data can be fully mined, the optimal slagging-off process can be calculated, the model robustness is high, the slagging-off time can be effectively shortened, molten iron waste is reduced, and the production benefits of enterprises are improved.

Description

technical field [0001] The invention relates to the technical field of desulfurization and slag removal of molten iron, in particular to a control method for automatic slag removal of molten iron based on deep learning. Background technique [0002] As steel mills pay more and more attention to energy saving and emission reduction, it is necessary to be efficient and reduce losses in each process section. In the hot metal desulfurization and slag removal process, there is still the phenomenon of hot metal waste and low efficiency. Reducing waste and improving efficiency have become the development goals of all enterprises. In this process, the most important link is the judgment of slag level, slag thickness and slag profile, which determines the final slag removal time and slag removal accuracy. The slag removal time and slag removal accuracy of desulfurization and slag removal determine the waste and efficiency of molten iron in this process section. Therefore, the accura...

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): G06K9/62G06N3/04G06N3/08C21C1/02C21C7/064
CPCG06N3/08C21C1/02C21C7/064C21C7/0087G06N3/045G06F18/24147G06F18/214
Inventor 张子豪李阳王胜勇刘晓健
Owner WISDRI WUHAN AUTOMATION
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