Process monitoring of iron-making process in a blast furnace with PCA-based methods. (February 2016)

• Record Type: Journal Article
• Authors: Zhou, Bo; Ye, Hao; Zhang, Haifeng; Li, Mingliang
• Is Part Of: Control engineering practice. Volume 47(2016)
• Page Start: 1
• Page End: 14
• Publication Date: 2016-02
• Subjects: Process monitoring -- Fault diagnosis -- Blast furnace -- Iron-making process -- Principal component analysis; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2015.11.006
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Incidents happening in the blast furnace will strongly affect the stability and smoothness of the iron-making process. Thus far, diagnosis of abnormalities in furnaces still mainly relies on the personal experiences of individual workers in many iron works. In this paper, principal component analysis (PCA)-based algorithms are developed to monitor the iron-making process and achieve early abnormality detection. Because the process exhibits a non-normal distribution and a time-varying nature in the measurement data, a static convex hull-based PCA algorithm (SCHPCA) which replaces the traditional T 2 -based abnormality detection logic with the convex hull-based abnormality detection logic, and its moving window version, called the moving window convex hull-based PCA algorithm (MWCHPCA) are proposed, respectively. These two algorithms are tested on the real process data to verify their effectiveness in the early abnormality detection of iron-making process.