Process monitoring of iron-making process in a blast furnace with PCA-based methods |
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Affiliation: | 1. Department of Chemical and Materials Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan; |
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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 T2-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. |
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Keywords: | Process monitoring Fault diagnosis Blast furnace Iron-making process Principal component analysis |
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