Steel leakage visualized characteristic forecasting method based on improved neural network

A neural network, BP neural network technology, applied in the field of iron and steel metallurgy continuous casting detection, can solve problems such as false positives

Active Publication Date: 2016-02-17
NORTHEAST DIANLI UNIVERSITY
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Problems solved by technology

This method improves the accuracy of the alarm and reduces the number of false alarms. However, due to the comple

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  • Steel leakage visualized characteristic forecasting method based on improved neural network
  • Steel leakage visualized characteristic forecasting method based on improved neural network
  • Steel leakage visualized characteristic forecasting method based on improved neural network

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Embodiment Construction

[0057] The present invention will be further described in detail through specific embodiments below in conjunction with the accompanying drawings.

[0058] figure 1 It is based on the improved BP neural network breakout visualized feature prediction block diagram. Depend on figure 1 It can be seen that a method based on the improved BP neural network breakout visual feature prediction method includes: extracting the visual features of the temperature anomaly area of ​​the breakout; establishing a three-layer BP artificial neural network breakout prediction model; Optimize; use the model to detect and predict the visual features of steel breakout online, and the detection steps are as follows:

[0059] 1) Crystallizer temperature rate thermal imaging and visualization feature extraction

[0060] figure 2 It is a schematic diagram of four crystallizer copper plates unfolded and thermocouples arranged. The length of the slab continuous casting mold is 900mm, and it is compo...

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Abstract

The invention discloses a steel leakage visualized characteristic forecasting method based on an improved neural network and belongs to the technical field of steel metallurgy continuous casting detection. The steel leakage visualized characteristic forecasting method specifically comprises the steps that a thermocouple temperature signal of a crystallizer copper plate is online detected, the temperature change rate of the crystallizer copper plate is visually presented through a thermal imaging technology; on the basis of searching and extracting of the area, temperature change, position, transmission rate and other characteristics of a temperature anomaly area, a back-propagation (BP) neural network steel leakage forecasting model is established; in addition, by virtue of the self-organization and self-adaptability of a genetic algorithm, the power value and threshold value of the model are optimized, so that crystallizer steel leakage visualized online detection and forecasting are achieved. According to the steel leakage visualized characteristic forecasting method, not only are the distribution, anomalous change and development trend of the crystallizer temperature visually presented, but also a crystallizer steel leakage accident can be prevented in real time accurately, so that the false alarm times are reduced, and the accuracy rate of a steel leakage forecasting system is improved.

Description

technical field [0001] The invention relates to a method for predicting visual characteristics of breakouts based on an improved neural network, and belongs to the technical field of iron and steel metallurgical continuous casting detection. Background technique [0002] Continuous casting is a key link in the modern steel production chain. Steel breakout is a major accident in continuous casting, which not only interrupts the continuous casting production process, but also interferes with the entire steelmaking production plan. The equipment brings different degrees of damage, causing huge economic losses. Therefore, how to detect and prevent steel breakout accidents has always been the focus of metallurgical continuous casting sites and metallurgical workers. [0003] Common types of breakouts include bonded breakouts, longitudinal cracked breakouts, pouring breakouts and corner breakouts, among which bonded breakouts occur most often, accounting for more than two-thirds ...

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Application Information

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IPC IPC(8): B22D11/18
CPCB22D11/182
Inventor 刘宇王旭东姚曼高亚丽狄驰张海波
Owner NORTHEAST DIANLI UNIVERSITY
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