Method for predicting crystallizer breakout based on agglomerative hierarchical clustering

Abstract

The invention discloses a method for predicting crystallizer breakout databased on agglomerative hierarchical clustering, and belongs to the technical field of steel metallurgy continuous casting detection. The method comprises the following steps that firstly, a sticking breakout / normal work condition sample database is established, the sticking breakout temperature and the normal work conditiontemperature are selected, and a sample database comprising a sticking breakout sample set and a normal work condition sample set is constructed; secondly, random sample set hierarchical clustering isconducted, equivalent samples are selected from the sticking breakout sample set and the normal work condition sample set at random, a random sample set is formed by the samples and online actual measurement temperature samples, and hierarchical clustering is conducted on the random sample set; and thirdly, breakout recognition and alarming are conducted, whether the online actual measurement temperature samples belong to the sticking breakout class cluster or not is detected, and accordingly breakout can be recognized and predicted. By means of the method, limitation of manual parameter defining in the predicting process is avoided, whether the online actual measurement temperature samples comprise the breakout features or not is judged only through respective features of the sticking breakout and the normal work condition temperature, and the beneficial effects that the detection principle is clear, the operation speed is high, and the breakout recognition accuracy rate is high are achieved.

Description

technical field [0001] The invention belongs to the technical field of iron and steel metallurgical continuous casting detection, and relates to a method for predicting a continuous casting crystallizer breakout based on agglomeration hierarchical clustering. Background technique [0002] Breakout is a catastrophic accident in the continuous casting process. It will not only cause the continuous casting machine to stop production, affect the operation rate and output, but also seriously damage the crystallizer, sector roller table and other equipment, causing huge economic losses and potential safety hazards. [0003] In order to ensure the smooth running of the continuous casting process, domestic and foreign countries have been devoting themselves to the development of mold breakout forecasting system. At present, a relatively mature and commonly used method is to embed and install armored thermocouples in the mold copper plate, and indirectly obtain the temperature change...

AI Technical Summary

Problems solved by technology

Compared with the patent CN101332499A, this invention is also based on the temperature change of the thermocouple to judge whether there is a steel breakout. The difference is that the thermocouple is installed near the cooling water outlet instead of the copper plate of the crystallizer, so the invention measures the cooling Compared with the method of installing a local thermocouple on the copper plate of the crystallizer, the instantaneous change of the overall cooling effect of the water is used to predict the steel breakout, and its sensitivity is lower, which will inevitably lead to a decrease in the accuracy of the prediction and an increase in the number of false alarms

[0006] In addition, the existing steel breakout prediction methods are highly dependent on human beings, and parameters such as alarm thresholds need to be manually set. When the casting steel type or process changes, the adjustment and optimization of relevant alarm parameters are very cumbersome. If it is not correct, the accuracy of the forecast will drop significantly, and even false negatives will occur.

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