Lipetsk State Technical University, Lipetsk, Russia

I. I. Shopin, Cand. Eng., Dept. of Metal Processing, e-mail: ShopinII@yandex.ru

The prospects for the next decade, both in the metallurgical industry as a whole and in rolling production, are a high level of competition in the face of falling demand for ordinary steel grades and rising energy prices. In turn, this prospect makes urgent the high need to improve the operating efficiency of rolling mills. The decisive advantage in such conditions will be the high rate of improvement in operational efficiency. Therefore, it is important to implement the most effective solutions in rolling production as quickly as possible. One of the sources for improving the operating efficiency of rolling mills is to reduce the number of negative technological events. And the best tool in the study of negative technological events is binary logistic regression, which allows you to solve classification problems. The key advantage over other methods for solving the classification problem (classification trees, random forest or gradient boosting) is the interpretability of the results obtained - the resulting mathematical formula can be easily checked for compliance with the physical meaning. The article presents a brief description of the binary logistic regression and shows how the assessment of the adequacy of the obtained mathematical model differs from the usual regression. It is shown that it is important to obtain not only an adequate and reliable mathematical model of the process, but also to correctly determine the decision threshold. Approaches to data preparation, analysis and use of results are demonstrated in order to reduce the number of negative technological events in rolling production. Practical results of using binary logistic regression to improve processes in the conditions of NLMK’s rolling facilities are reported.

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