Soft sensors based on nonlinear steady-state data reconciliation in the process industry

被引:45
作者
Schladt, Markus [1 ]
机构
[1] BASF AG, WLE ED, Competence Ctr Automat Technol, D-67059 Ludwigshafen, Germany
关键词
soft sensor; data reconciliation; rigorous modeling; gross error detection; steady-state detection; selectivity estimation; distillation column;
D O I
10.1016/j.cep.2006.06.022
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
in this article, recent practical experiences of soft sensor projects based on data reconciliation in the process industry are reported. The discussed items comprise the steady-state detection, the gross error detection, the initial value generation, and remarks on the implementation of this method. Two practical examples are presented from the area of process industry. The first is a representative soft sensor application to provide consistent measurements for data monitoring. The second is a soft sensor application which uses a rigorous model to estimate concentrations in a distillation column. (c) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码:1107 / 1115
页数:9
相关论文
共 19 条
[1]  
BEBAR M, 2005, REGELGUTEBEWERTUNG K
[2]  
BHAT S, 2003, P AICHE 2003 SPRING
[3]   Critical values for a steady-state identifier [J].
Cao, SL ;
Rhinehart, RR .
JOURNAL OF PROCESS CONTROL, 1997, 7 (02) :149-152
[4]   AN EFFICIENT METHOD FOR ONLINE IDENTIFICATION OF STEADY-STATE [J].
CAO, SL ;
RHINEHART, RR .
JOURNAL OF PROCESS CONTROL, 1995, 5 (06) :363-374
[5]   RECONCILIATION OF PROCESS FLOW-RATES BY MATRIX PROJECTION .2. THE NONLINEAR CASE [J].
CROWE, CM .
AICHE JOURNAL, 1986, 32 (04) :616-623
[6]   Data reconciliation - Progress and challenges [J].
Crowe, CM .
JOURNAL OF PROCESS CONTROL, 1996, 6 (2-3) :89-98
[7]  
Helsel D.R., 1992, STAT METHODS WATER R, V49
[8]  
Himmelblau D. M., 1970, PROCESS ANAL STAT ME
[9]   A NONPARAMETRIC TREND TEST FOR SEASONAL DATA WITH SERIAL DEPENDENCE [J].
HIRSCH, RM ;
SLACK, JR .
WATER RESOURCES RESEARCH, 1984, 20 (06) :727-732
[10]   Techniques for solving industrial nonlinear data reconciliation problems [J].
Kelly, JD .
COMPUTERS & CHEMICAL ENGINEERING, 2004, 28 (12) :2837-2843