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Continuous casting breakout prediction method based on neural network

A neural network and breakout technology, applied in the field of metallurgical casting, can solve the problems of low breakout prediction accuracy, and achieve the effects of improving the recognition effect and prediction accuracy, reducing the false alarm rate and omission rate, and improving the calculation speed.

Inactive Publication Date: 2010-10-06
PANGANG GROUP VANADIUM TITANIUM & RESOURCES +3
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Problems solved by technology

[0004] In order to solve the problem of low precision of steel breakout prediction existing in the existing continuous casting breakout prediction method, the purpose of the present invention is to provide a neural network-based continuous casting leak Steel Forecast Method

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  • Continuous casting breakout prediction method based on neural network
  • Continuous casting breakout prediction method based on neural network
  • Continuous casting breakout prediction method based on neural network

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Embodiment Construction

[0013] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0014] Such as figure 1 As shown, according to an embodiment of the present invention, the continuous casting breakout prediction method based on neural network may include:

[0015] Step 1: Collect the temperature data of the thermocouples in the continuous casting site online and store the temperature data. Such as Figure 2-3 As shown in , there are multiple thermocouples arranged in a matrix on the mold of the continuous casting machine, such as figure 2 exemplarily shows that 6 rows and 7 columns are installed on the two wide sides of the crystallizer (including the fixed side and the movable side), and 7 rows and 2 columns are installed on the two narrow sides of the crystallizer, and the thermocouples are evenly distributed, In this way, a total of 112 thermocouples cover the entire crystallizer copper plate, which can realize the temperature detec...

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Abstract

The invention discloses a continuous casting breakout prediction method based on a neural network, which comprises the following steps that: step 1: the temperature data of a thermocouple on a continuous casting site is collected on line and saved; step 2: the temperature data is pre-processed; step 3: the temperature data which is collected from any one thermocouple and is pre-processed is input a single-couple time sequence network breakout prediction model, the output value of the single-couple time sequence network breakout prediction model is compared with the maximum determination threshold, and if the output value of the model is more than the maximum determination threshold, the breakout is predicted; and the method is characterized in that genetic algorithm is used for initializing the connection weights and thresholds of the single-couple time sequence network breakout prediction model. The method can improve the identification effect and the prediction precision of a continuous casting bonding breakout process so as to greatly reduce the false prediction rate and missing prediction rate.

Description

technical field [0001] The invention relates to the field of metallurgical casting, in particular to a neural network-based continuous casting breakout prediction method. Background technique [0002] Breakout refers to the phenomenon that in the initial stage of continuous casting or during the pouring process, the solidification of the slab shell is not good, or the slab shell is broken or leaked due to other external forces, so that the internal molten steel flows out. Breakout is one of the most harmful production accidents in the continuous casting production process. It not only directly affects the continuous casting production, but also seriously damages molds, roller tables and other equipment, increases the amount of maintenance and maintenance costs, and causes huge economic losses. , there is a huge security risk. In the breakout accidents caused by various reasons, cohesive breakouts account for about 65% to 80% of the total number of breakouts, so reducing coh...

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Application Information

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IPC IPC(8): B22D11/18
Inventor 祭程陈永朱苗勇吴国荣杨吉林李桂军蔡兆镇曾建华杨素波
Owner PANGANG GROUP VANADIUM TITANIUM & RESOURCES
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