An online application of dynamic PLS to a dearomatization process

被引:81
作者
Komulainen, T
Sourander, M
Jämsä-Jounela, SL
机构
[1] Aalto Univ, Lab Proc Control & Automat, FIN-02015 Helsinki, Finland
[2] Neste Engn Oy, FIN-06101 Porvoo, Finland
关键词
online process monitoring; industrial real-time application; dynamic PLS; computed variables; time-lagged variables; trickle-bed reactor; online analysers; flash point analyser; distillation curve analyser;
D O I
10.1016/j.compchemeng.2004.07.014
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Early detection of process disturbances and prediction of malfunctions in process equipment improve the safety of the process, minimize the time and resources needed for maintenance, and increase the uniform quality of the products. The objective of online-monitoring is to trace the state of the process and the condition of process equipment in real-time, and to detect faults as early as possible. In this article the different properties of the online-monitoring methods applied in the process industries are first reviewed. A description of the systematic development of the online-monitoring system for an industrial dearomatization process, specifically for flash point and distillation curve analysers, is then presented. Finally, the results of offline and online tests of the monitoring system using real industrial data from the Fortum Naantali Refinery in Finland, are described and discussed. The developed online-monitoring application was successful in real-time process monitoring and it fulfilled the industrial requirements. (C) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码:2611 / 2619
页数:9
相关论文
共 24 条
[1]  
Alhoniemi E, 1999, INTEGR COMPUT-AID E, V6, P3
[2]  
Berglund A, 1997, J CHEMOMETR, V11, P141, DOI 10.1002/(SICI)1099-128X(199703)11:2<141::AID-CEM461>3.0.CO
[3]  
2-2
[4]   A multivariate statistical controller for on-line quality improvement [J].
Chen, G ;
McAvoy, TJ ;
Piovoso, MJ .
JOURNAL OF PROCESS CONTROL, 1998, 8 (02) :139-149
[5]   Recursive exponentially weighted PLS and its applications to adaptive control and prediction [J].
Dayal, BS ;
MacGregor, JF .
JOURNAL OF PROCESS CONTROL, 1997, 7 (03) :169-179
[6]   Nonlinear principal component analysis - Based on principal curves and neural networks [J].
Dong, D ;
McAvoy, TJ .
COMPUTERS & CHEMICAL ENGINEERING, 1996, 20 (01) :65-78
[7]   PARTIAL LEAST-SQUARES PATH MODELING WITH LATENT-VARIABLES [J].
GERLACH, RW ;
KOWALSKI, BR ;
WOLD, HOA .
ANALYTICA CHIMICA ACTA-COMPUTER TECHNIQUES AND OPTIMIZATION, 1979, 3 (04) :417-421
[8]   RECURSIVE ALGORITHM FOR PARTIAL LEAST-SQUARES REGRESSION [J].
HELLAND, K ;
BERNTSEN, HE ;
BORGEN, OS ;
MARTENS, H .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1992, 14 (1-3) :129-137
[9]   PRINCIPAL COMPONENTS AND FACTOR-ANALYSIS .1. PRINCIPAL COMPONENTS [J].
JACKSON, JE .
JOURNAL OF QUALITY TECHNOLOGY, 1980, 12 (04) :201-213
[10]   A process monitoring system based on the Kohonen self-organizing maps [J].
Jämsä-Jounela, SL ;
Vermasvuori, M ;
Endén, P ;
Haavisto, S .
CONTROL ENGINEERING PRACTICE, 2003, 11 (01) :83-92