Using Principal Component Analysis and Least Squares Support Vector Machine to Predict the Silicon Content in Blast Furnace System

Authors

  • Shihua Luo School of Statistics, Center of Applied Statistics, Jiangxi University of Finance & Economics, Nanchang, 330013
  • Tianxin Chen School of Statistics, Center of Applied Statistics, Jiangxi University of Finance & Economics, Nanchang, 330013
  • Ling Jian School of Science, China University of Petroleum, Qingdao, 266555

DOI:

https://doi.org/10.3991/ijoe.v14i04.8397

Keywords:

blast furnace system, principal component analysis, least squares support vector machine, silicon content prediction

Abstract


Blast furnace system is a typical example of complex industrial system. The silicon ([Si]) content in blast furnace system is an important index to reflect the temperature of furnace. Therefore, it is significant to carry out an accurate predictive control of furnace temperature. In this paper a composite model combining Principal Component Analysis (PCA) and Least Squares Support Vector Machine (LSSVM) is established to predict the furnace temperature. At the very beginning, in order to avoid redundancy and excessive noise pollution, PCA method is applied to reduce the dimensionality of original input variables. Secondly, the dimension-reduced variables are introduced to predict the silicon content by applying the LSSVM model. Finally, the result is compared with direct multivariable LSSVM prediction. The simulation results show that the new algorithm has positive significance as it achieves more obvious prediction hit rate (more than 80%) than direct multivariable LSSVM (with rate lower than 75%).

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Published

2018-04-26

How to Cite

Luo, S., Chen, T., & Jian, L. (2018). Using Principal Component Analysis and Least Squares Support Vector Machine to Predict the Silicon Content in Blast Furnace System. International Journal of Online and Biomedical Engineering (iJOE), 14(04), pp. 149–162. https://doi.org/10.3991/ijoe.v14i04.8397

Issue

Vol. 14 No. 04 (2018)

Section

Papers

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