Data-Driven Robust RVFLNs Modeling of a Blast Furnace Iron-Making Process Using Cauchy Distribution Weighted M-Estimation

Ping Zhou, Youbin Lv, Hong Wang, Tianyou Chai

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77 Scopus citations

Abstract

Optimal operation of a practical blast furnace (BF) iron-making process depends largely on a good measurement of molten iron quality (MIQ) indices. However, measuring the MIQ online is not feasible using the available techniques. In this paper, a novel data-driven robust modeling is proposed for an online estimation of MIQ using improved random vector functional-link networks (RVFLNs). Since the output weights of traditional RVFLNs are obtained by the least squares approach, a robustness problem may occur when the training dataset is contaminated with outliers. This affects the modeling accuracy of RVFLNs. To solve this problem, a Cauchy distribution weighted M-estimation-based robust RFVLNs are proposed. Since the weights of different outlier data are properly determined by the Cauchy distribution, their corresponding contribution on modeling can be properly distinguished. Thus, robust and better modeling results can be achieved. Moreover, given that the BF is a complex nonlinear system with numerous coupling variables, the data-driven canonical correlation analysis is employed to identify the most influential components from multitudinous factors that affect the MIQ indices to reduce the model dimension. Finally, experiments using industrial data and comparative studies have demonstrated that the obtained model produces a better modeling and estimating accuracy and stronger robustness than other modeling methods.

Original languageEnglish
Article number7887735
Pages (from-to)7141-7151
Number of pages11
JournalIEEE Transactions on Industrial Electronics
Volume64
Issue number9
DOIs
StatePublished - Sep 2017
Externally publishedYes

Funding

Manuscript received December 16, 2016; revised February 9, 2017; accepted February 28, 2017. Date of publication March 27, 2017; date of current version August 9, 2017. This work was supported in part by the National Natural Science Foundation of China under Grant 61473064, Grant 61290323, Grant 61621004, and Grant 61333007, in part by the Research Funds for the Central Universities under Grant N160805001 and Grant N160801001, and in part by the Project on Scientific Research for the EDLN under Grant L20150186. (Corresponding author: Ping Zhou.) P. Zhou, Y. Lv, and T. Chai are with the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China (e-mail: [email protected]; [email protected]; [email protected]).

Keywords

  • Blast furnace (BF) iron making
  • Cauchy distribution
  • M-estimation
  • canonical correlation analysis (CCA)
  • random vector functional-link networks (RVFLNs)
  • robust RVFLNs

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