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Data-based Latent Variable Methods for Process Analysis&--44; Monitoring and Control

This presentation gives an overview of methods for utilizing large process data matrices. These data matrices are almost always of less than full statistical rank, and therefore latent variable methods are shown to be well suited to obtaining useful subspace models from them for treating a variety of important industrial problems. An overview of the important concepts behind latent variable models is presented and the methods are illustrated with industrial examples in the following areas: (i) the analysis of historical databases and trouble-shooting process problems; (ii) process monitoring and FDI; (iii) monitoring and control using digital imaging; (iv) process control of batch processes in reduced dimensional subspaces. In each of these problems latent variable models provide the framework on which solutions are based.

Type of Seminar:
Public Seminar
Prof. John F. MacGregor
McMaster Advanced Control Consortium, Department of Chemical EngineeringMcMaster University, Hamilton, Ontario, Canada, L8S 4L7
May 25, 2004   17:15

ETH Zentrum, Gloriastrasse 35, Zurich, Building ETZ, Room E6
Contact Person:

Prof. M. Morari
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Biographical Sketch:
JOHN MACGREGOR received his PhD degree in Statistics, his MSc degrees in Statistics and in Chemical Engineering from the University of Wisconsin, Madison, and his Bachelor of Engineering degree from McMaster University, Hamilton, ON, Canada. After working in industry for several years as a process specialist with Monsanto Company in Texas, he joined McMaster University in 1972 as an Assistant Professor in the Department of Chemical Engineering. He is currently holds the title of “University Professor” as well as the Dofasco Chair in Process Automation and Information Technology at McMaster University. He is a co-founder of the McMaster Advanced Control Consortium that is sponsored by many international companies. Dr. MacGregor’s research interests have spanned a wide range of areas, from polymer reaction engineering to process systems engineering, and statistical methods. In recent years he has concentrated on the development of multivariate statistical methods for use in process monitoring, fault detection, and control using the very large multivariate data-bases available from industrial processes. This multivariate research includes problems in both continuous and batch processes as well as image analysis methods.