One day workshop on

Process and Control System Performance Monitoring and Trend Analysis (T-1)

scheduled with

2000 American Control Conference 
June 28-30, 2000 
Hyatt Regency Hotel, Chicago, Illinois, USA 

To find more about ACC 2000 and registration for the conference and the workshop, please visit http://che.vill.edu/acc2000/acc.html

Instructors:
Ali Cinar; Illinois Institute of Technology
Ahmet Palazoglu; University of California, Davis
Ferhan Kayihan; IETek 

Tuesday June 27, 2000

This workshop presents the key ideas in process monitoring & trend analysis for multivariable continuous & batch processes; controller performance assessment is also included. Classical statistical process control tools such as Shewhart and EWMA charts are reviewed for comparison with advanced methods that are more appropriate for multivariable processes. Several techniques such as principal components analysis (PCA), partial least squares (PLS), canonical variate state space (CVSS) modeling, Wavelet analysis, and Hidden Markov Models (HMM) are introduced to provide mathematical background for monitoring tasks. PCA and PLS based methods are presented for monitoring multivariable continuous processes, while contribution plots are used for assisting fault diagnosis. Trend detection using qualitative signal representation and HMM classification, wavelet-based hidden Markov trees for state estimation and trend classification, and multidimensional classification problems are also included. 

Multiway PCA and three-way (PARAFAC and Tucker) methods for monitoring multivariable batch processes at the end of the batch and for real-time process monitoring during the progress of the batch are presented. Methods for monitoring of multistage continuous and batch processes are outlined while two dimensional data analysis and controller performance assessment methods applicable for sheet processes is introduced. Monitoring and performance assessment of a single feedback loop, a single loop with feedback and feedforward controllers, multivariable controllers and model predictive controllers is discussed. Stand-alone software tools for process monitoring and trend analysis and tools integrated with supervisory real-time knowledge-based systems is also illustrated. Examples and case studies from chemical, biotechnology and pharmaceutical, paper, polymer, and metals manufacturing industries are included. 

SCHEDULE:

TUESDAY, JUNE 27
08:30 - 09:00     INTRODUCTION
09:00 - 10:20     MATHEMATICAL TECHNIQUES
10:30 - 12:00     MONITORING & TREND ANALYSIS IN CONTINUOUS PROCESSES
13:30 - 14:20     MONITORING OF BATCH (AND FED-BATCH) PROCESSES
14:30 - 15:50     MONITORING OF SHEET AND WEB PROCESSES
16:10 - 17:00     CONTROLLER PERFORMANCE ASSESSMENT
17:10 - 18:00     IMPLEMENTATION AND SOFTWARE; CONCLUSIONS; DISCUSSIONS 

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