Abstract
An improved BP neural network model was presented by modifying the learning algorithm of the traditional BP neural network, based on the Levenberg-Marquardt algorithm, and was applied to the breakout prediction system in the continuous casting process. The results showed that the accuracy rate of the model for the temperature pattern of sticking breakout was 96.43%, and the quote rate was 100%, that verified the feasibility of the model.
Highlights
Continuous Casting is the process whereby molten steel is solidified into a semi finished slab, or some such elementary form, for subsequent rolling in the finishing mills[1]
In this paper, based on the indepth study of a variety of breakout prediction systems, a breakout prediction system has been constructed based on a BP (Back Propagation) neural network model which was optimized with LM (Levenberg Marquardt) algorithm
The prediction of the sticking type breakout is to make the right judgments of the temperature patterns which may cause leakage
Summary
Continuous Casting is the process whereby molten steel is solidified into a semi finished slab, or some such elementary form, for subsequent rolling in the finishing mills[1]. Breakouts are of major concern in the continuous steel-casting process, because they can lead to severe damage to equipment, significant process downtime, and potential safety consequences. The loss caused by a typical breakout accident is close to 200,000 dollars[2]. Among many forms of breakouts, the sticking-type breakout has the highest incidence and the greatest harm, it is very critical to prevent the sticking type breakout occurrence for reducing incidence of breakout accident. In this paper, based on the indepth study of a variety of breakout prediction systems, a breakout prediction system has been constructed based on a BP (Back Propagation) neural network model which was optimized with LM (Levenberg Marquardt) algorithm. The system was trained and tested with the data collected at the continuous casting production site
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