Multivariate statistical process control of an industrial fluidised-bed reactor

被引:37
作者
Simoglou, A [1 ]
Martin, EB [1 ]
Morris, AJ [1 ]
机构
[1] Newcastle Univ, Ctr Proc Analyt & Control Technol, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
基金
英国工程与自然科学研究理事会;
关键词
multivariate quality control; data compression; prediction; estimation; chemical variables control;
D O I
10.1016/S0967-0661(00)00015-0
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Multivariate statistical process control (MSPC)is a tool for the comprehensive monitoring of the performance of a manufacturing process. There is now a real need to demonstrate the applicability of MSPC to complex manufacturing processes and highlight the benefits that can be derived from its implementation. Alongside this, is the increasing interest in predicting quality or important chemical quality variables associated with product yield and production. This paper demonstrates the performance monitoring potential of MSPC and the predictive capability of canonical variates analysis and projection to latent structures by application to an industrial fluidised-bed reactor. (C) 2000 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:893 / 909
页数:17
相关论文
共 29 条
[1]  
Anderson T., 1984, INTRO MULTIVARIATE S
[2]   PLS regression methods [J].
Höskuldsson, Agnar .
Journal of Chemometrics, 1988, 2 (03) :211-228
[3]  
GALLAGHER N, 1997, DEV BENCHMARKING MUL, P78
[4]   PARTIAL LEAST-SQUARES REGRESSION - A TUTORIAL [J].
GELADI, P ;
KOWALSKI, BR .
ANALYTICA CHIMICA ACTA, 1986, 185 :1-17
[5]  
Hotelling H., 1947, TECHNIQUES STAT ANAL, P111
[6]  
HWANG DH, 1998, IFAC WORKSH ON LIN F
[7]   CONTROL PROCEDURES FOR RESIDUALS ASSOCIATED WITH PRINCIPAL COMPONENT ANALYSIS [J].
JACKSON, JE ;
MUDHOLKAR, GS .
TECHNOMETRICS, 1979, 21 (03) :341-349
[8]   GAUSSIAN APPROXIMATION TO DISTRIBUTION OF A DEFINITE QUADRATIC FORM [J].
JENSEN, DR ;
SOLOMON, H .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1972, 67 (340) :898-902
[9]  
Johnson R. A., 1992, APPL MULTIVARIATE ST, V4
[10]  
Jolliffe I., 2002, PRINCIPAL COMPONENT, DOI [DOI 10.1016/0169-7439(87)80084-9, 10.1007/0-387-22440-8_13, DOI 10.1007/0-387-22440-8_13]