Multivariable Dynamic Modeling for Molten Iron Quality Using Incremental Random Vector Functional-link Networks

作     者：Li ZHANG Ping ZHOU He-da SONG Meng YUAN Tian-you CHAI 

作者机构：State Key Laboratory of Synthetical Automation for Process IndustriesNortheastern University Department of Mechanical EngineeringUniversity of Melbourne 

出 版 物：《Journal of Iron and Steel Research International》 (国际钢铁研究杂志)

年 卷 期：2016年第23卷第11期

页      面：1151-1159页

核心收录：

学科分类：080602[工学-钢铁冶金] 08[工学] 0806[工学-冶金工程] 

基　　金：Item Sponsored by National Natural Science Foundation of China(61290323,61333007,61473064) Fundamental Research Funds for Central Universities of China(N130108001) National High Technology Research and Development Program of China(2015AA043802) General Project on Scientific Research for Education Department of Liaoning Province of China(L20150186) 

主　　题：molten iron quality multivariable incremental random vector functional-link network blast furnace iron-making data-driven modeling principal component analysis 

摘      要：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 off-line analysis through laboratory sampling. Focusing on the practical challenge, a data-driven modeling method was presented for the prediction of MIQ using the improved muhivariable incremental random vector functional-link net- works （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-out- put （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 estima ting accuracy than other MIQ modeling methods.