Author/Authors :
Gang Chen، نويسنده , , Thomas J. McAvoy and Michael J. Piovoso، نويسنده ,
DocumentNumber :
1384250
Title Of Article :
A multivariate statistical controller for on-line quality improvement
شماره ركورد :
11107
Latin Abstract :
Producing good quality products is an important process control objective. However, achieving this objective can be very difficult in a continuous process, especially when quality measurements are not available on-line or they have long time delays. In this paper, a control approach using multivariate statistical models is presented tc achieve this objective. The goal of the control approach is to decrease variations in product quality wihout real time quality measurements. A PCA model which incorporates time lagged variables is used, and the control objective is expressed in the score space of this PCA model. A controller is designed in the model predictive control (MPC) framework, and it is used to control the equivalent score space repres~ntation of the process. The score predictive model for the MPC algorithm is built using partial least squares (PLS). The proposed controller can be developed from and implemented on top of existing PI]) control systems, and it is demonstrated in two case studies, which involve a binary distillation column c.nd the Tennessee Eastman process.
From Page :
139
NaturalLanguageKeyword :
multivariate statisti!cs , PLS , multi-way PCA , mpc , Process control
JournalTitle :
Studia Iranica
To Page :
149
To Page :
149
Link To Document :
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