Paper Title:
Improved Online Prediction of Silicon Content in Ironmaking Process Using Support Vector Regression with Novel Outlier Detection
  Abstract

Various data-based soft sensor models have been established for online prediction of the silicon content in a pig iron. Actually, modeling data often contain many outliers and this can deteriorate the quality of models. However, little attention is paid to efficient outlier detection. Besides, most of traditional outlier detection methods are assumed that data are distributed (approximately) normally and thus they might be invalid for some situations. A new multivariate preprocessing method for outlier detection without any assumption of data distribution is proposed. This novel outlier detection method mainly utilizes a support vector clustering (SVC) strategy. After SVC-based preprocessing, a support vector regression soft sensor model is built. A comparative study for an industrial blast furnace is investigated and the results show its superiority.

  Info
Periodical
Advanced Materials Research (Volumes 154-155)
Edited by
Zhengyi Jiang, Xianghua Liu and Jinglong Bu
Pages
251-255
DOI
10.4028/www.scientific.net/AMR.154-155.251
Citation
Y. Liu, H. Q. Yu, Z. L. Gao, P. Li, "Improved Online Prediction of Silicon Content in Ironmaking Process Using Support Vector Regression with Novel Outlier Detection", Advanced Materials Research, Vols. 154-155, pp. 251-255, 2011
Online since
October 2010
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Price
$32.00
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