A decoupled multiple model approach for soft sensors design

被引：22

作者：

Domlan, Elom ^{[1
]}

Huang, Biao ^{[1
]}

Xu, Fangwei ^{[2
]}

Espejo, Aris ^{[2
]}

机构：

[1] Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2G6, Canada

[2] Syncrude Canada Ltd, Ft Mcmurray, AB T9H 3L1, Canada

来源：

CONTROL ENGINEERING PRACTICE | 2011年 / 19卷 / 02期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Soft sensor; Multiple model; Fuzzy model; Multi-rate; IDENTIFICATION; SYSTEMS;

D O I：

10.1016/j.conengprac.2010.10.006

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 the application of a multiple model approach for the design of a soft sensor aiming at predicting the quality of the product of a separation unit in oil sands processing. The variable of interest here for the product quality is the percentage of water present in the product stream and the goal of the soft sensor is to provide an alternative mean for obtaining on-line measurements of the water-content value. The most reliable measurement of the water-content is obtained through laboratory analysis that introduces delays and long sampling time in the availability of the water-content data. These constraints generate a multi-rate sampling problem for the soft sensor design. The usage of a decoupled multiple model structure allows handling the multi-rate problem and designing a dynamical prediction model for the water-content. The effectiveness of the designed soft sensor in predicting the quality of the product stream is illustrated by on-line implementation results. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：126 / 134

页数：9

共 26 条

[1] Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models [J].

Abonyi, J ;

Babuska, R ;

Szeifert, F .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2002, 32 (05) :612-621

[2]

[Anonymous], 1998, INT SER INTELL TECHN

[3]

[Anonymous], 1987, Unconstrained Optimization: Practical Methods of Optimization

[4] Fuzzy modeling of enzymatic penicillin-G conversion [J].

Babuska, R ;

Verbruggen, HB ;

van Can, HJL .

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 1999, 12 (01) :79-92

[5] PIECEWISE LINEAR IDENTIFICATION OF NONLINEAR-SYSTEMS [J].

BILLINGS, SA ;

VOON, WSF .

INTERNATIONAL JOURNAL OF CONTROL, 1987, 46 (01) :215-235

[6] Exploring process data with the use of robust outlier detection algorithms [J].

Chiang, LH ;

Pell, RJ ;

Seasholtz, MB .

JOURNAL OF PROCESS CONTROL, 2003, 13 (05) :437-449

[7]

Dougan P., 1997, IEEE Industry Applications Society 1997 Dynamic Modeling Control Applications for Industry Workshop. Record of Workshop Papers, P68, DOI 10.1109/DMCA.1997.603553

[8]

Filev D., 1991, International Journal of Approximate Reasoning, V5, P281, DOI 10.1016/0888-613X(91)90013-C

[9]

Fortuna L, 2007, ADV IND CONTROL, P1, DOI 10.1007/978-1-84628-480-9

[10]

Gasso K, 2001, IEEE DECIS CONTR P, P2992, DOI 10.1109/CDC.2001.980732

← 1 2 3 →