A dynamic subspace model for predicting burn-through point in iron sintering process

被引：22

作者：

Cao, Weihua ^{[1
,2
]}

Zhang, Yongyue ^{[3
]}

She, Jinhua ^{[1
,2
,4
]}

Wu, Min ^{[1
,2
]}

Cao, Yuan ^{[3
]}

机构：

[1] China Univ Geosci, Sch Automat, Wuhan 430074, Hubei, Peoples R China

[2] Hubei Key Lab Adv Control & Intelligent Automat C, Wuhan, Hubei, Peoples R China

[3] Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China

[4] Tokyo Univ Technol, Sch Engn, Tokyo 1920982, Japan

来源：

INFORMATION SCIENCES | 2018年 / 466卷

基金：

中国国家自然科学基金;

关键词：

Burning-through point; Subspace modeling method; Dynamic model; Sintering process; CONTROL TECHNOLOGY; IDENTIFICATION; OPTIMIZATION; SYSTEMS;

D O I：

10.1016/j.ins.2018.06.069

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a dynamic modeling method for predicting the exhaust-gas temperature (EGT) of the burn-through point (BTP) in an iron sintering process. First, a subspace modeling method is used to build a steady-state subspace model (SSSM) for the EGT at a steady state. Then, a dynamic subspace model (DSM) that is driven by the errors of the SSSMs is developed to improve the accuracy of the EGT prediction in a continuous process. Finally, a grid search dynamic subspace model (GSDSM) is established to find the best parameters for each SSSM in the DSM. Verification results show that the GSDSM yields a predicted EGT with a high precision, which can be implemented in a predicting controller an actual sintering process. (C) 2018 Elsevier Inc. All rights reserved.

引用

页码：1 / 12

页数：12

共 28 条

[1] Improved subspace identification with prior information using constrained least squares [J].

Alenany, A. ;

Shang, H. ;

Soliman, M. ;

Ziedan, I. .

IET CONTROL THEORY AND APPLICATIONS, 2011, 5 (13) :1568-1576

[2] Experience in industrial plant model development using large-scale artificial neural networks [J].

Boger, Z .

INFORMATION SCIENCES, 1997, 101 (3-4) :203-216

[3] Dynamic Calibration of Adaptive Optics Systems: A System Identification Approach [J].

Chiuso, Alessandro ;

Muradore, Riccardo ;

Marchetti, Enrico .

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2010, 18 (03) :705-713

[4] Recursive Predictor-Based Subspace Identification With Application to the Real-Time Closed-Loop Tracking of Flutter [J].

Houtzager, Ivo ;

van Wingerden, Jan-Willem ;

Verhaegen, Michel .

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2012, 20 (04) :934-949

[5]

Jamaludin IW, 2013, 2013 IEEE 9TH INTERNATIONAL COLLOQUIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS (CSPA), P140, DOI 10.1109/CSPA.2013.6530030

[6] Focus on: Subspace-based Identification and Its Applications [J].

Johansson, Rolf .

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2009, 23 (12) :1051-1052

[7] Realization of stochastic systems with exogenous inputs and subspace identification methods [J].

Katayama, T ;

Picci, G .

AUTOMATICA, 1999, 35 (10) :1635-1652

[8]

Li MH, 2006, 2006 1ST IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, VOLS 1-3, P857

[9]

[李幼凤 LI Youfeng], 2006, [化工学报, Journal of Chemical Industry and Engineering (China)], V57, P473

[10] Recursive subspace identification of linear and non-linear Wiener state-space models [J].

Lovera, M ;

Gustafsson, T ;

Verhaegen, M .

AUTOMATICA, 2000, 36 (11) :1639-1650

← 1 2 3 →