Mixed model judgement based crystallizer breakout predication method

Abstract

The invention discloses a mixed model judgment based crystallizer breakout predication method. The mixed model judgment based crystallizer breakout predication method comprises the following steps of:1) acquiring real-time temperatures of thermocouples inside a crystallizer, and sending the real-time temperatures to a computer terminal; 2) correcting the acquired temperature values and modelling,and converting the temperature values into an DIB image to store; 3) performing discretization on the DIB image, and obtaining crystallizer temperature fields through a thermal imaging system; 4) performing image identification and classification on a temperature field distribution image, and judging whether the temperature of each thermocouple of the crystallizer is normal or not; and 5) comprehensively judging whether the temperature has anomaly or not, and judging the anomaly reasons according to the image identification results and the results of a comprehensive expert system, thereby predicating whether a crystallizer breakout phenomenon appears or not. The mixed model judgment based crystallizer breakout predication method realizes accurate predication and warning of crystallizer breakout during continuous-casting production, and improves quality of continuously cast slabs.

Description

technical field [0001] The invention belongs to the field of smelting and continuous casting production, in particular to a method for predicting steel breakout of a crystallizer in continuous casting production. Background technique [0002] In the continuous casting process, the crystallizer is an efficient heat transfer device, and its main function is to export the heat of the molten steel, so that the molten steel forms a shell with a certain thickness after leaving the crystallizer. In the production process, the speed and thickness of the shell formed by the mold, the geometric dimensions of the shell formed, and the degree of lubrication of the mold wall by the mold slag all have a major impact on the quality of the continuous casting billet. [0003] If there are factors such as poor lubrication between the mold and the continuous casting slab, uneven vibration of the mold, unstable casting speed, and uneven cooling, it will cause molten steel to seep from the wall ...

AI Technical Summary

Problems solved by technology

The disadvantage of this system is that both the neural network and the genetic algorithm model need a large amount of data as the training set. In the early stage of the production system, the training samples of the model are insufficient and the data is perfect, so the entire breakout prediction system will exist. false alarm

In the actual production process, when the thermocouple fails or the temperature of the crystallizer fluctuates greatly, the model will have false positives and negative negatives, and cannot achieve a high reporting rate.

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