Short Course

Process Monitoring and Identification of

Dynamic Systems using Statistical Techniques

Instructors:
Wallace E. Larimore; Adaptics, Inc,
Michael J. Piovoso, DuPont Company
Dale E. Seborg, University of California, Santa Barbara


Monday/Tuesday, May 4-5, 1998, 8:30am - 5:30pm


Location:
Days-Inn Hotel at Philadelphia International Airport (to become a Sheraton Hotel as of 1 April 1998)
4101 Island Avenue
Philadelphia, PA 19153, USA
Telephone: 215 492-0400; Fax: 215 365-6035

SYNOPSIS

In this short course, the theory and application of statistically-based monitoring and identification techniques will be considered. Course attendees will also have an opportunity to gain hands-on experience with commercial software packages such as the PLS_Toolbox (for PCA & PLS applications) and the ADAPTx software for Automated Multivariable System Identification.

Traditional process monitoring in industrial plants is based on comparing measurements to specified limits, and on the experience of the plant personnel. More recently, statistical quality control techniques (SQC) methods have been widely used for product quality control. However, the standard SQC methods are based on the assumptions that the process dynamics are negligible and that the process disturbances are uncorrelated. But these restrictions are not valid for many real-time monitoring problems where the process dynamics are important and correlated disturbances are the rule, rather than the exception. Thus, more advanced monitoring methods are required for these applications. The potential payoff is the early detection of small changes in the process that can be corrected or compensated to reduce their ultimate impact on process performance.

In this short course, the traditional methods of process monitoring and statistical quality control will be reviewed, and several advanced methods for handling process dynamics and autocorrelated errors will be critically evaluated. These advanced methods include principal component analysis (PCA), projection to latent structures (PLS), and canonical variate analysis (CVA). They are based on multivariate statistics, time series analysis, and system identification. Numerous simulation and experimental examples are presented to illustrate key issues and to provide comparisons.

A new automated method is presented for identifying the system dynamics and autocorrelated error structure based on canonical variate analysis (CVA), a generalization of the PCA and PLS methods. The CVA approach allows for the completely automatic modeling of the process dynamics and error structure. From a software user's viewpoint, the modeling of the process dynamics and errors is no more difficult than fitting a static multivariate regression model. The CVA method is statistically optimal even in the presence of known or unknown feedback paths that are often present in process applications.

The PCA, PLS, and CVA method are applied to a number of process monitoring problems using both simulated and industrial data, and direct comparison are made among these and alternative monitoring methods. Attendees are encouraged to bring their laptop computers for instruction in the use of two software packages. Using the PLS_Toolbox, the PCA and PLS methods will be applied to data. The ADAPTx software package for Automated Multivariable System Identification will be used for identification in the presence of feedback and colored disturbance processes, and assessing the model accuracy and adequacy of the input excitation. Those unable to bring a laptop computer will be paired with someone having a computer so that they can learn the use of the software.

COURSE OUTLINE

  1. Overview of Process Monitoring Strategies
  2. Statistical Approaches to Process Monitoring
  3. Applications of Multivariate Statistical Monitoring Techniques
  4. Identification of Linear Dynamic Models from Input-Output Data
  5. Canonical Variate Analysis (CVA) Approach to Process Identification
  6. Process Monitoring Using Dynamic Models
  7. Computer Software Instruction

Course Fee and Deadlines

The registration fee is $600 for reservations received on or before March 31, 1998 and $750 for reservations received after March 31. University students may register for $300. Full refunds will be made for cancellations received before March 31. A $200 cancellation penalty will be charged for cancellations received after March 31.

Course Registration

There is limited space available for the Short Course. Please return the Registration Form as soon as possible to reserve a space, and contact the Hotel to make a reservation if you also need a room. Please fill out the enclosed registration form and send it with payment to Dr. Larimore. His contact information is:


Dr. Wallace E. Larimore, President
Adaptics, Inc
1717 Briar Ridge Road
McLean, VA 22101
Phone: 703 532-0062; Fax: 703 536-3319
larimore@adaptics.com
Web Site: http://www.adaptics.com

Hotel Room Registration

A block of rooms at the Days Inn has been reserved for the Short Course at the reduced rate of $89 per night. In order to get the reduced rate, you must make a reservation directly with the hotel no later than March 31,1998. Their contact information is:


Days-Inn Hotel at Philadelphia International Airport (to become a Sheraton Hotel as of 1 April 1998)
4101 Island Avenue
Philadelphia, PA 19153, USA
Telephone: 215 492-0400; Fax: 215 365-6035

Further Information: Contact either Dr. Larimore or Professor Seborg (seborg@engineering.ucsb.edu).

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