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| Multivariable Dynamic Modeling for Molten Iron Quality Using Incremental Random Vector Functional-link Networks |
| Li ZHANG1,Ping ZHOU1,He-da SONG1,Meng YUAN2,Tian-you CHAI1 |
1. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, Liaoning, China
2. Department of Mechanical Engineering, University of Melbourne, Melbourne 3010, Victoria, Australia |
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Abstract Molten iron temperature as well as Si, P, and S contents is the most essential molten iron quality (MIQ) indices in the blast furnace (BF) ironmaking, which requires strict monitoring during the whole ironmaking production. However, these MIQ parameters are difficult to be directly measured online, and large-time delay exists in offline analysis through laboratory sampling. Focusing on the practical challenge, a data-driven modeling method was presented for the prediction of MIQ using the improved multivariable incremental random vector functional-link networks (M-I-RVFLNs). Compared with the conventional random vector functional-link networks (RVFLNs) and the online sequential RVFLNs, the M-I-RVFLNs have solved the problem of deciding the optimal number of hidden nodes and overcome the overfitting problems. Moreover, the proposed M-I-RVFLNs model has exhibited the potential for multivariable prediction of the MIQ and improved the terminal condition for the multiple-input multiple-output (MIMO) dynamic system, which is suitable for the BF ironmaking process in practice. Ultimately, industrial experiments and contrastive researches have been conducted on the BF No��2 in Liuzhou Iron and Steel Group Co. Ltd. of China using the proposed method, and the results demonstrate that the established model produces better estimating accuracy than other MIQ modeling methods.
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Received: 09 December 2015
Published: 04 November 2016
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| Fund:the National Natural Science Foundation of China;the Fundamental Research Funds for the Central Universities |
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Corresponding Authors:
Ping Zhou
E-mail: zhouping@mail.neu.edu.cn
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2016, Vol. 23 
