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AN ADVANCE ALARMING SYSTEM FOR LONGITUDINAL FACIAL CRACKS AT CORNER, PREDICTING, PRIOR TO FORMATION, OF CRACKS THAT LEAD TO BREAKOUTS IN THIN SLAB CONTINUOUS CASTING
AN ADVANCE ALARMING SYSTEM FOR LONGITUDINAL FACIAL CRACKS AT CORNER, PREDICTING, PRIOR TO FORMATION, OF CRACKS THAT LEAD TO BREAKOUTS IN THIN SLAB CONTINUOUS CASTING
The invention relates to the continuous casting process that is one of the most important links in the steel making chain as it represents transition in steel processing from liquid to solid state, and due to its inherent technical challenges impacting on stability, productivity & quality. One of these challenges relates to "breakouts" where the formative solid shell encapsulating liquid ruptures in the mold leading to drainage of the internal liquid and consequent blockage of equipment and process. In thick (conventional) casters "stickers", i.e. shell sticking to mold, are the predominant cause of breakouts and almost all such casters come equipped with "Breakout Detection Systems" (BDS) that tap signals from an array of mold-embedded thermocouples and process them in real time to recognize specific patterns that precede sticker breakouts. On recognition an alarm is generated with consequent reduction in casting speed and mitigation of fault. In thin slab casters conventional BDS functionality is hamstrung by the fact that "stickers" constitute only a minor proportion of all breakouts and casting speeds are so high that the time between sensing of a fault by thermocouples and its mitigation by speed reduction is sometimes insufficient for complete cure and may still result in breakout. This method fulfills the need for an advance prediction system for faults in thin slab casters that are attuned specifically to types of faults characteristic of such casters and furthermore, prediction is sufficiently in advance to enable successful mitigation by responsive action. In particular, this system attends to breakouts of type "Longitudinal Facial Crack at Corner". It feeds online multiple casting process parameters into an Artificial Neural Network (ANN) which delivers in real time a value of "Abnormality Index" (AI). This AI is further tracked to recognize sudden & continuous increase which is indicative of sharp deterioration of casting and an impeding fault whence an alarm is generated followed by mitigating action. The system has been confirmed to work with high accuracy, i.e. capturing most faults amid low false positives.
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