detection and diagnosis of plant-wide oscillations: an application study
DESCRIPTION
Detection and Diagnosis of Plant-wide Oscillations: An Application Study. Vinay Kariwala M.A.A. Shoukat Choudhury, Sirish L. Shah, J. Fraser Forbes, Edward S. Meadows Department of Chemical and Materials Engineering University of Alberta. Hisato Douke, Haruo Takada - PowerPoint PPT PresentationTRANSCRIPT
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Detection and Diagnosis of Plant-wide Oscillations: An Application Study
Vinay Kariwala M.A.A. Shoukat Choudhury, Sirish L. Shah,
J. Fraser Forbes, Edward S. Meadows Department of Chemical and Materials Engineering
University of Alberta
Hisato Douke, Haruo TakadaMitsubishi Chemical Corporation,
Mizushima, Japan
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2Outline
• Problem Description
• Detection• Theory (Autocorrelation function)
• Application Results
• Diagnosis• Theory (Valve Stiction)
• Application Results
• Future Directions
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3Problem Description
Feed
Condenser
Top ProductReflux Drum
Side Stripper
Stripper
Distillation Column
Oscillations in
Condenser Level
Bottom Product
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4Problem Description
• Condenser Level
• Oscillations with Large amplitude
• Back-off from Optimal operating point
• Economic Potential
1% increase in set points ~ 20M Yen/year
• Previous attempts
• PID tuning, MPC model
• Not successful
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5
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6Scope of Analysis
Column 1
FC8
LC10
LC9
FC7
LC8
FC6
FC5
LC7
LI1
LC2
PC1
LI2
LC1
FI1
FI3
AC1
FC2
TC1
AC2
LC3
FI2
LC4
LC5
LI3
SI1
PC2 PC
3
PC4
PC5
TI4 TI
5
TI6
TC2
FC3
FC4
LC11
TI1
Column 2
Compressor
LC6
TI2
TI3
FC1
FI4
Heat Exchanger
Chain
LI5
LI4
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7Data Description
Data Set: 2880 samples, 1 min. data,
Variables: 45 Tags + 15 Controller Outputs (MV)
• 15 SISO control loops• 5 cascade control loops• 2 DMCs
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8Detection Philosophy
• Which variables are oscillating?• Which variables have common
oscillations?
• Important to find• All variables with common oscillations
• Root cause likely to lie within this set
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9Detection by Visual Inspection
Fourier Transform
Time trends
Presence of Oscillation – Peak in Spectra
Period and Regularity – Difficult to Judge
• Multiple oscillations destroy Regularity• Noise overshadows Oscillations
Power Spectrum
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10Detection using ACFAuto Correlation
Function
Effect of Noise Reduced
ACF oscillates at same frequency as signal
Regularity of oscillations – Zero Crossings of ACF
Power SpectrumTime Trend
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11Detection using ACF
Period of Oscillation Oscillation regular if
ACF
ZeroCrossings
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12Clustering using ACF
Two signals – same frequency oscillation if
Ref: Thornhill et al., JPC, 2003
Oscillation considered significant if
(Power in selected band)/(Power in entire spectrum) >
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13Multiple Oscillations
Fourier Transform
Two peaks in Spectra
Use Band pass filters
Calculate ACF for each filtered signal
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14Detection Algorithm
Remove Non-stationary trends
Repeat if more than one oscillations present in every filter range OR stop
Detect and cluster oscillations
Narrow ranges of band pass filters around detected oscillations
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15Detection: ResultsLow frequency range• 158 min./cycle – 27 tags• 137 min./cycle – 10 tags
Medium frequency range• 62 min./cycle – 11 tags• 75 min./cycle – 23 tags • 86 min./cycle – 5 tags
High frequency range• 43 min./cycle – 5 tags • 25 min./cycle – 1 tag• 4 min./cycle – 1 tag
Condenser Level
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16Low frequency detections
158 samples/cycle
137 samples/cyclePV OP PV OP
Column 1
FC8
LC10
LC9
FC7
LC8
FC6
FC5
LC7
LI1
LC2
PC1
LI2
LC1
FI1
FI3
AC1
FC2
TC1
AC2
LC3
FI2
LC4
LC5
LI3
SI1
PC2 PC
3
PC4
PC5
TI4 TI
5
TI6
TC2
FC3
FC4
LC11
TI1
Column 2
Compressor
LC6
TI2
TI3
FC1
FI4
Heat Exchanger
Chain
LI5
LI4
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17Summary of Detection
• Low frequency oscillations• 158 minute/cycle• 26 tags other than condenser level
• Plant wide nature of oscillations revealed
• Root cause should lie in this set
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18
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19Possible Reasons
• Poorly tuned Controller
• External disturbances
• Process induced oscillations
• Valve Problems
• MPC model mismatch
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20Definition of Stiction
valv
e ou
tput
(m
v)
valve input (op)
deadband stickband
slip jump, j
stickband + deadband
mov
ing
phas
e
A BC
D
EF
G
s
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21
Central Idea:Nonlinear interactions between different frequencies
Normalized Bispectrum – squared Bicoherence
Test of Nonlinearity
Bispectrum DFT
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22Linear and nonlinear Signal
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23
Non-Gaussianity Index and Nonlinearity Index
NGI <= 0 NGI>0 , NLI=0
NGI>0, NLI>0
Frequency independent Frequency dependent
GaussianLinear
Non-GaussianLinear
Non-GaussianNonlinear
Critical Values of bic2crit is determined at 95% or 99%
confidence interval of the squared bicoherence
Test of Non-linearity (cont’d)
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24
NGI = 0.02 andNLI = 0.55
200 400 600 800 10001.1
1.12
1.14
x 104
PV
and S
P
PVSP
200 400 600 800 100038
38.2
38.4
38.6
38.8
CO
sampling instants
CO
Loop is Nonlinear
1. The process is locally linear in the current operating region
2. Disturbances entering the loop are linear
Assumptions:
Flow Control Loop in a Refinery
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25
OP
PV
PV
OP
Pattern of Stiction in PV-OP Plot
apparent stiction = maximum width of the cycles in pv-op plot
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26
4
38.1 38.2 38.3 38.4 38.5 38.6 38.7 38.8 38.91.105
1.11
1.115
1.12
1.125
1.13
1.135
1.14
1.145x 10
P Q
a b
OP
PV
Quantification of Apparent Stiction
Apparent Stiction = 0.35 %
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27Nonlinearity Analysis
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28Stiction QuantificationFC5 PC1 TC2
No Stiction 0.5% 1.25%
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29Research Directions
• ACF based Detection Algorithm– False Detection, Premature Termination
• Stiction Quantification– Assumption of linear disturbance
• Path Analysis– Oscillation Propagation
• Model Predictive Controller– Oscillations due to model mismatch
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30Acknowledgements
• NSERC
• Dr. Nina Thornhill, UK
• Ebara San, Amano San, Oonodera San
• Computer Process Control group