期刊文献+

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

Multivariable Dynamic Modeling for Molten Iron Quality Using Incremental Random Vector Functional-link Networks
分享 导出
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 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.2in 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. 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.
作者 Li ZHANG Ping ZHOU He-da SONG Meng YUAN Tian-you CHAI Li ZHANG;Ping ZHOU;He-da SONG;Meng YUAN;Tian-you CHAI;State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University;Department of Mechanical Engineering,University of Melbourne;
出处 《钢铁研究学报:英文版》 SCIE EI CAS CSCD 2016年第11期1151-1159,共9页 Journal of Iron and Steel Research
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)
molten iron quality multivariable incremental random vector functional-link network blast furnace iron-making data-driven modeling principal component analysis
作者简介 Biography:Li ZHANG, Master; E-mail:15702416802@163. corn; Corresponding Author: Ping ZHOU, Doctor, Associate Professor; E-mail: zhouping@mail. neu. edu. cn
  • 相关文献
投稿分析

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部 意见反馈