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Mathematical Problems in Engineering
Volume 2015, Article ID 326160, 11 pages
http://dx.doi.org/10.1155/2015/326160
Research Article

Data-Driven Dynamic Modeling for Prediction of Molten Iron Silicon Content Using ELM with Self-Feedback

1State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
2Control System Center, Manchester University, Manchester M60 1QD, UK

Received 21 August 2014; Revised 17 November 2014; Accepted 18 November 2014

Academic Editor: Jiuwen Cao

Copyright © 2015 Ping Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Silicon content ([Si] for short) of the molten metal is an important index reflecting the product quality and thermal status of the blast furnace (BF) ironmaking process. Since the online detection of [Si] is difficult and larger time delay exists in the offline assay procedure, quality modeling is required to achieve online estimation of [Si]. Focusing on this problem, a data-driven dynamic modeling method is proposed using improved extreme learning machine (ELM) with the help of principle component analysis (PCA). First, data-driven PCA is introduced to pick out the most pivotal variables from multitudinous factors to serve as the secondary variables of modeling. Second, a novel data-driven ELM modeling technology with good generalization performance and nonlinear mapping capability is presented by applying a self-feedback structure on traditional ELM. The feedback outputs at previous time together with input variables at different time constitute a dynamic ELM structure which has a storage ability to tackle data in different time and overcomes the limitation of static modeling of traditional ELM. At last, industrial experiments demonstrate that the proposed method has a better modeling and estimating accuracy as well as a faster learning speed when compared with different modeling methods with different model structures.