Guest EDITORIAL
By Michael J. Piovoso and Karlene A. Hoo
Multivariate Statistics for Process Control By Michael J. Piovoso and Karlene A. Hoo
Piovoso (
[email protected]) is with Penn State Great Valley School of Graduate Professional Studies, 30 E. Swedesford Rd., Malvern, PA 19355, U.S.A. Hoo is with the Department of Chemical Engineering, Texas Tech University, Lubbock, TX 79409, U.S.A.
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liked the idea and encouraged us to proceed. We decided to pursue this possibility and searched for a competitive business within DuPont to demonstrate the concept. We were fortunate to find James P. Yuk, an analytical chemist who had an appreciation for chemometrics and for the concept of using these ideas to monitor the state of well-being of a process. He was a real inspiration to us. The approach we undertook was to gather data during periods in which the process performance was exceptional. Unfortunately, what also dominated the normal operations was throughput for feed rate changes. To address this complication, we first performed a PLS analysis to remove the effect of the rate changes. We then performed a PCA analysis on the residuals to develop a PCA model to establish what we called the “process sweet spot.” We believe we were the first to use this term in this context, by which we meant “where the process preferred to live” and still produce a product whose quality was within the acceptable limits. With this model, we then went online to monitor the chemical system. Jim Yuk called the PCA monitor a “software analyzer” of the process. The PCA model gave information as to the variability of process operation within the model space. New data were projected onto the subspace defined by the PCA loadings. Using the Mahalanobis distance, the size of the projections was then compared to a 95% statistical upper bound found from the
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ur experience with the application of multivariate statistics to process control began in the late 1980s. Bruce Kowalski, founder of the Center for Process Analytic Chemistry (CPAC), had coined the term chemometrics to describe the application of mathematics and statistics to chemical processes. At that time, the authors were employed by the DuPont Chemical Co., which was a member of CPAC. Bruce educated the members of CPAC about principal component analysis (PCA) and projections to latent structures, also known as partial least squares (PLS). In September 1989, Prof. Harald Martens of the Norwegian University of Science and Technology was invited to give a short course on chemometrics at DuPont. He had just completed a draft of his book Multivariate Calibration, which he co-authored with Tormod Naes. Harald was also about to embark on the development of a software package called “Unscrambler” with his new company. About the same time, we had discussed the application of this technology to monitoring a chemical process within DuPont. Harald
October 2002
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normal process operations. Information about the data outside the model space was also available in the residuals between the actual process measurements and the reconstruction based on the PLS/PCA models. The magnitude of the residuals could be compared against a 95% confidence threshold. When either of these statistical measures exceeded their upper bounds, contribution plots were generated. Contribution plots gave the contribution of each variable to the values computed. Variables whose behavior was inconsistent with the normal process data were readily identified, and operators could then take corrective actions. This new software analyzer became very popular with the operating personnel, and other businesses became interested in pursuing similar studies. This work was first published in IEEE Transactions on Instrumentation and Measurement in 1992 (vol. 41), and a more in-depth analysis appeared in “The Use of Multivariate Statistics in Process Control,” chapter 33 of The Control Handbook, Levine and Dorf, Eds. (Boca Raton, FL: CRC, 1996). This special section of the Magazine contains four articles that cover a broad spectrum of activities in the use of chemometrics for process control. Dr. Theodora Kourti of the McMaster Advanced Control Consortium has put together an excellent review of the state of the art in this technology. This article presents the foundations for PCA and PLS, together with several examples based on the work done at McMaster University to illustrate the applications of the technology. For those unfamiliar with the concepts and approaches in multivariate statistics, this article will provide useful background for the subsequent articles. The article by Dr. Elaine Martin, Dr. Julian Morris, and Dr. Steven Lane, titled “Monitoring Process Manufacturing Performance,” illustrates the concepts of process monitoring with several different applications. A significant problem in the monitoring of continuous or batch processes is that the data are not only multivariable but also serially correlated. This article discusses the issues involved with these data and an approach to addressing this situation. In addition, the students and researchers at the University of Newcastle Upon Tyne, under the direction of Prof. Julian Morris, have investigated multigrade, multirecipe batch processes. The article describes these ideas in some depth. The third article is by Dr. Cenk Ündey and Dr. Ali Çinar and is titled “Statistical Monitoring of Multistage, Multiphase Batch Processes.” The authors describe a methodology for monitoring the overall performance of a batch process at the end of the batch while providing diagnosis of problematic stages. Multistage means that in a single processing unit, successions of events occur that result from operational or phenomenological changes. Multiphase means that steps in the operation occur in different processing units. A case study of a pharmaceutical granule
October 2002
production system illustrates how quality and monitoring might be combined. Finally, Dr. Ashish Singhal and Dr. Dale Seborg describe the application of process monitoring tools to a different type of problem. Oftentimes a problem develops in the operation of a system, and there may be some vague recollection that this problem occurred at some point in the past. Having a system that could go through a historical database and seek instances in which the process data behaved in a fashion similar to that experienced during the process upset would be very useful to the engineer trying to troubleshoot the process. This article addresses this very problem. The collection of articles in this special section spans a wide range of interest in the technology associated with the application of multivariate statistics for process control. The articles provide an opportunity to learn the concepts involved and how the technology can be applied. We hope you enjoy the section. Michael J. Piovoso received his B.S. in electrical engineering from the University of Delaware in 1964, his M.S. in electrical engineering from the University of Michigan in 1965, and his Ph.D. from the University of Delaware in 1969. After serving in the U.S. Army, he joined E.I. DuPont de Nemours & Co., Inc., in 1972. In 1999, he was awarded the IEEE Control Systems Technology Award. After retiring from DuPont, he joined Penn State University, where he is an Associate Professor of Electrical Engineering. In 2001, he was awarded the Penn State Great Valley School of Graduate Professional Studies research award. His research interests are in the application of multivariate statistics for control, neural networks, data mining, and nonlinear control. He is an associate editor of IEEE Control Systems Magazine. Karlene A. Hoo received her B.S. from the University of Pennsylvania in 1981 and her M.S. in 1983 and Ph.D. in 1986 from the University of Notre Dame, all in chemical engineering. From 1986 to 1988 she worked for Exxon Chemical Co. in New Jersey and in 1988 joined the engineering services division of the DuPont Chemical Co. in Wilmington, DE. In 1994 she joined the Department of Chemical Engineering at the University of South Carolina, Columbia, as an assistant professor. She is currently an associate professor and graduate advisor for the Department of Chemical Engineering at Texas Tech University, Lubbock. She also co-directs the process control and optimization consortium that is funded by the chemical and petrochemical industries and leading consulting companies. In 2001 she was the recipient of a Halliburton Award for teaching and research excellence. Her interests include fundamental chemical plant design and simulation, system identification, multivariate statistical analysis, model-based control, and neural networks. She is an associate editor of IEEE Control Systems Magazine.
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