Application on determination of key factors of hot metal silicon content based on multivariate statistics

Abstract

This paper use multivariate statistics to analyze and research the influencing factors of the hot metal silicon content, and apply principal component analysis to determine the key factors. According to blast furnace refining mechanism and production practice, determine 16 relevant operating parameters of hot metal silicon content, set up matrix and model, get the contribution rate of each variable to the principal, find out six key factors and use T2 statistics monitor hot metal silicon content changes in the actual production situation.