The evaluation of the steelmaking process using statistical indicators and regression models

The steelmaking process using Basic Oxygen Furnace (BOF) is one of the essential processes within the metallurgical technological chain. This process's most important quality output parameters are the steel's chemical composition and the steel's temperature, which are essential in terms of further processing of the steel. These parameters are affected by input parameters of the process such as, e.g., temperature, weight and the composition of pig iron, weight and the composition of steel scrap, amount of blown oxygen, etc. The statistical evaluation methods of various processes belong amongst basic qualitative methods that enable us to analyze and compare data on various quantitative parameters. Moreover, they make it possible to design appropriate interventions to improve these processes. The paper describes using statistical indicators for analyzing of BOF process and proposes of statical regression model for the temperature of the melt.

I. Introduction

Currently, the metallurgical industry, especially steel production, as one of the primary parts of this industry, occupies an important place in the Slovak economy. The steel industry annually produces millions of tons of steel for domestic and foreign markets. However, the quality and quantity of steel produced still needs to be increased to be able to compare ourselves on the global scale of this industry. In order to increase the quality of products in technological and production processes, it is necessary to know these processes, to correctly measure the process parameters, and, last but not least, to control these processes.