Transcript Document

Overview of Current Research Activities
S. Joe Qin
Department of Chemical Engineering
The University of Texas at Austin
Austin, Texas 78712
512-471-4417
[email protected]
Control.che.utexas.edu/qinlab
February 19, 2001
Current Group Members
 Ph.D. students (expected graduation date)
– Sergio Valle (May 2001)
– Chris McNabb (May 2002, Weyerhaeuser)
– Henry Potrykus (May 2003)
– Jennifer Wang
– Elaine Hale
– Greg Cherry
– Rick Good
– Q. Peter He
– Weilu Lin
– Robert Chong (AMD)
 M.S. students
– Ricky Mak (August 2001)
– Chadi Saade
 Post-doctoral Associate
– Ricardo Dunia
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Current Projects
 Chemical process data based monitoring and control
– NSF, DuPont
 Dynamic process monitoring and control in
microelectronics
– NSF, AMD
 Process fault detection and identification
– Texas ARP, DuPont, Union Carbide
 MIMO control performance monitoring – WeyCo
 Subspace identification for stochastic systems
 MPC of DAE chemical processes
 Modeling of directed evolution (with Dr. G. Georgiou)
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General Activities
 MPC Technology Survey --- with T.A. Badgwell
– Updated nonlinear MPC applications survey as a book chapter, “A
Review of Nonlinear Model Predictive Control Applications," to be
included in a book published by the Institution of Electrical Engineers,
edited by Basil Kouvaritakis
– Put together a linear and nonlinear MPC technology survey paper
• Around 3700 applications till mid-1999
• Vendors included: Adersa, Aspen, Honeywell, MDC, PCL, CC, DOT,
Pavilion
• To be submitted to Control Sys. Magazine for wide readership.
 Qin Sabbatical, 2001-2002
– Plan to work at AMD at least half time on run-to-run control and FDC
– Plan to visit Lennart Ljung and other groups in the world on subspace
identification and control
– Approved and partially funded by UT-Austin
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Process Monitoring and Diagnosis
 Review of the area
– The process monitoring and diagnosis area has been developed well in
the last ten years. An overview is being written by Dunia and Qin
 Plant-wide, hierarchical monitoring and diagnosis
– Large scale process monitoring cannot be done effectively using one
monolithic PCA/PLS model.
– Variable blocking naturally divides the process into sections for which
hierarchical monitoring is much more effective
– Confidence limits are developed for hierarchical contribution plots,
which are tested effectively on a polyester film process.
– Four different multi-block PCA/PLS algorithms are unified with the
traditional PCA/PLS algorithms
– Papers:
• Qin, S.J., S. Valle and M. Piovoso (2001). On unifying multi-block analysis with
application to decentralized process monitoring. To appear on J. Chemometrics.
• S. Valle, Qin, S.J., and M. Piovoso (2001). Fault Detection and Diagnosis in Industrial
Processes, ACC-01, June 2001.
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Monitoring and Diagnosis (continued)
 Multi-scale PCA for early fault detection
– A unique combination of PCA and wavelets is proposed to form a
specific multi-scale PCA in order to detect time and scale features from
the data for early fault detection.
– Successfully applied to data from a tubular reactor process at Carbide
– Papers:
• Misra, M., S.J. Qin, H. Yue and C. Ling (2001). Multivariate process monitoring and fault
identification using multi-scale PCA, Being revised for Comput. Chem. Engng.
• Misra, M., S.J. Qin, H. Yue and C. Ling (1999). Multivariate Sensor Data Validation and
Compression Using Multi-scale PCA. Presented at AIChE Annual Meeting, Dallas, TX.
 Combined index for fault detection and identification
– The traditional SPE and T-square indices are useful but sometimes
conflicting and confusing
– A combined index provides a complete measure of normal situation and
is successfully applied to an industrial process
–
Yue, H. and S.J. Qin (2001). Reconstruction based fault identification using a combined index.
Revised for I&EC Research.
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Monitoring and Diagnosis (continued)
 Dynamic sensor validation
– Unique sensor fault isolation is desirable in fault detection and gross error
detection
– A structured residual approach is proposed which maximizes the sensitivity for
fault isolation.
– Subspace identification model is used for the normal process model
–
Qin, S. J. and W. Li (2001). Detection and identification of faulty sensors in dynamic processes with
maximized sensitivity, Revised for AIChE Journal.
 Sensor validation under MPC feedback
– Developed and tested the idea of validating sensors under MPC feedback using
a simulated FCC unit
– Effectively provided that the fault detection is faster than the controller
– Applied to a refinery process successfully in Indonesia; detected an equipment
failure well ahead of operator’s notice
– Papers:
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Nugroho, Toto and S.J. Qin (2001). Sensor validation under feedback control of MPC, accepted by Control
Engineering Practice.
Nugroho, Toto and S.J. Qin (2001). Mutivariate Statistical Process Monitoring: Application in Residual Catalytic
Cracking Unit, to be submitted to Control Sys. Magazine.
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Subspace Identification (SMI)
 Represents a new class of methods that produce a general
state space model from process data
 Most existing methods deal with output error only, ignoring
input errors completely
 A PCA-based SMI method is proposed to deal with SMI for
error-in-variables formulation
– Consistency achieved by using instrumental variables
– PCA projection matrix contains all model information
– Paper presented at AIChE Annual Meeting, LA, 2000 and submitted
to J. of Process Control.
– Jennifer Wang will present the results at this meeting
 Ad hoc Dynamic PCA vs. subspace identification
– The time-lagged dynamic PCA (Ku et al., 1995) is not consistent in
the presence of measurement noise
–
Li, W. and S.J. Qin (2000). Consistent dynamic PCA based on errors-in-variables subspace
identification. To appear in J. of Process Control.
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Semiconductor Process Applications
 APC/FDC Short Course/Tutorial
– Run to Run Control and Fault Detection. Short course presented at
AEC/APC Symposium XII, September 23-28, 2000, Tahoe, CA, with
Tom Edgar and J. Campbell.
– Fault detection and classification theory for the user --- Tutorial.
Presented at AEC/APC Symposium XII, September 23-28, 2000, Tahoe,
CA.
 Plasma etching endpoint detection and fault diagnosis
– Work done at AMD using Peak Sensors OES system
– Papers:
• Yue, H., S.J. Qin, J. Wiseman, and A. Toprac (2001). Plasma etching
endpoint detection using multiple wavelengths for small open-area wafers.
J. of Vacuum Science & Technology, 19, 66-75.
• H. Yue, S.J. Qin, R. Markle, C. Nauert, and M. Gatto (2000). Fault detection of plasma
etchers using optical emission spectra. IEEE Trans. on Semiconductor Manufacturing, 13,
374-385.
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MIMO Control Performance Monitoring
 Interactor matrix and factorization based MIMO monitoring
methods are developed by Huang and Shah, Ko and Edgar, and
Harris et al.
 Projection based monitoring --- Chris McNabb
– Using subspace concept and subspace identification to calculate the
performance directly
– State space interpretation of MIMO time delay structure
– Applying to a boiler process
– Extendable to MPC monitoring where non-square control is typical
– Work in progress
 Inner-outer factorization for monitoring --- Tom Edison
– Use pole-zero concepts in MIMO systems
– Deal with non-minimum phase directly
– Work in progress
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MPC for DAE systems --- Henry Potrykus
 DAE systems are typical in chemical process modeling, control
and optimization (Marquardt, Allgower, Biegler, et al.)
 We study MPC for DAEs including higher index DAEs in a
systematic manner
– Control or optimization problems with active constraints may be treated
as DAE systems
– Singularly perturbed systems may be attacked by understanding the
limiting DAEs
– We are developing a program for finding the optimal control for general
DAE systems, which could be extended to MPC design
– Henry plans to visit Allgower at University of Stuttgart for a year
starting this summer
– Work in progress
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Modeling of Directed Evolution – R. Dunia
 To produce enzymes with improved functions, Dr. Georgiou’s
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group has generated tons of genetic data from directed
evolution (error-prone PCR)
The process is a mixture of binomial and Poisson processes
Mathematical models are needed since simulation will be out
of memory very quickly
It is desirable to study the genetic difference between clones
with improved functions and those without
PCA is applied with promising results, but we have to deal with
categorical data (A, T, G, C)
Work in progress
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