NDLI: Data-driven recursive subspace identification based online modelling for prediction and control of molten iron quality in blast furnace ironmaking

Content Provider	IET Digital Library
Author	Zhou, Ping Dai, Peng Song, Heda Chai, Tianyou
Abstract	In blast furnace ironmaking operation, the molten iron temperature and the silicon content ([Si]) are two key molten iron quality (MIQ) indices. The measurement, modelling and control of these indices have always been of importance in metallurgic engineering and automation. In this study, data-driven methods for online prediction and control of multivariate MIQ indices are proposed by integrating hybrid modelling and control techniques together. First, a data-driven hybrid method that combines canonical correlation analysis and correlation analysis is proposed to identify the most influential controllable variables as the modelling inputs from multitudinous factors. Then a data-driven online model for prediction of MIQ is established by recursive subspace identification (R-SI) with forgetting factor. Unlike the conventional SI modelling, the proposed R-SI-based online modelling only identifies subspace matrices for a data-driven input–output model without explicitly estimating the system matrices, which can reduce the computation complexity. Finally, a predictive controller is designed to maintain the MIQ indices at an expected level by using the developed MIQ prediction model as an online predictor. Since the parameters of the predictor are updated adaptively by the latest process data, the predictive controller can produce more reliable and stable control performance. Experiments using industrial data have verified the superiority and practicability of the proposed methods.
Starting Page	2343
Ending Page	2351
Page Count	9
ISSN	17518644
Volume Number	11
e-ISSN	17518652
Issue Number	Issue 14, Sep (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/11/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2016.1474
Journal	IET Control Theory & Applications
Publisher Date	2017-05-31
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Blast Furnace Ironmaking Blast Furnaces Canonical Correlation Analysis Computational Complexity Control Application in Metallurgical Industries Control System Analysis Control System Synthesis Control Technology And Theory Correlation Analysis Correlation Method Data-driven Hybrid Method Data-driven Input-output Model Data-driven Recursive Subspace Identification Based Online Modelling Forgetting Factor Inspection And Quality Control Iron Matrix Algebra Metallurgic Automation Metallurgic Engineering Metallurgical Industries MIQ Prediction Model Modelling And Identification Molten Iron Quality Control Molten Iron Quality Prediction Molten Iron Temperature Multitudinous Factors Multivariable Control System Multivariate MIQ Indices Control Online Prediction Optimal Control Predictive Control Predictive Controller Design Quality Control R-SI-based Online Modelling Recursive Estimation Recursive SubSpace Identification SI Modelling Silicon Content Simulation SubSpace Matrix Synthesis Method
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Human-Computer Interaction Electrical and Electronic Engineering Computer Science Applications

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