Journal
NEUROCOMPUTING
Volume 387, Issue -, Pages 139-149Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2020.01.030
Keywords
Robust modeling; Robust stochastic configuration networks; Kernel density estimation; Multi-input multi-output modeling; Blast furnace ironmaking; Molten iron quality
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Funding
- National Natural Science Foundation of China [61890934, 61790572, 61290323]
- Liaoning Revitalization Talents Program [XLYC1907132]
- Research Funds for the Central Universities [N180802003]
- State (Beijing) Key Laboratory of Process Automation in Mining Metallurgy [BGRIMM-KZSKL-2017-04]
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Blast furnace ironmaking (BFI) is currently the most widely used method of pig iron smelting. In order to achieve efficient and reasonable control, how to quickly and accurately obtain the molten iron quality (MIQ) model is a key issue. Aiming at this problem, this paper applies robust stochastic configuration networks (RSCNs) based on kernel density estimation (KDE) into the BFI modeling to obtain the MIQ model with good modeling accuracy and strong robustness quickly and effectively. Firstly, the network model is incrementally constructed by adding neurons one by one using the conventional SCNs algorithm. Secondly, in order to solve the problem of insufficient robustness of conventional SCNs, kernel density estimation algorithm is introduced to obtain the corresponding probability density estimates of each training set, and it's used as the penalty weight introduced into constructing process of conventional SCNs. At the same time, the network output weight is obtained by an improved method to solve the problem that the output weight of the conventional RSCNs is abnormal in the multi-output modeling application. Finally, modeling experiments based on actual industrial data of BFI production verified that RSCNs can achieve good modeling accuracy and strong robust performance. (C) 2020 Elsevier B.V. All rights reserved.
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