detection and diagnosis of plant wide oscillations an application study n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Detection and Diagnosis of Plant-wide Oscillations: An Application Study PowerPoint Presentation
Download Presentation
Detection and Diagnosis of Plant-wide Oscillations: An Application Study

Loading in 2 Seconds...

play fullscreen
1 / 30

Detection and Diagnosis of Plant-wide Oscillations: An Application Study - PowerPoint PPT Presentation


  • 105 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Detection and Diagnosis of Plant-wide Oscillations: An Application Study' - adam-monroe


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
detection and diagnosis of plant wide oscillations an application study

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

Mitsubishi Chemical Corporation,

Mizushima, Japan

outline
Outline
  • Problem Description
  • Detection
    • Theory (Autocorrelation function)
    • Application Results
  • Diagnosis
    • Theory (Valve Stiction)
    • Application Results
  • Future Directions
problem description
Problem Description

Condenser

Feed

Reflux Drum

Top Product

Stripper

Side Stripper

Bottom Product

Oscillations in

Condenser Level

Distillation Column

problem description1
Problem 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
slide5

Plant-wide

Oscillation Detection

scope of analysis

Heat

Exchanger

Chain

Column 2

FC

4

FC

3

LC

10

LC

9

LC

8

LC

7

FC

8

TC

2

FC

7

FC

5

FC

6

LI

5

LI

4

LI

1

LC

3

LC

6

LC

11

PC

1

FI

2

LC

2

AC

2

TI

1

TI

4

TI

5

PC

4

PC

5

PC

2

TC

1

PC

3

LC

1

LC

5

LI

3

LC

4

LI

2

FC

1

FI

1

Compressor

SI

1

FC

2

FI

3

AC

1

Column 1

TI

3

TI

6

TI

2

FI

4

Scope of Analysis
data description
Data 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
detection philosophy
Detection 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
detection by visual inspection
Detection by Visual Inspection
  • Multiple oscillations destroy Regularity
  • Noise overshadows Oscillations

Fourier

Transform

Time trends

Power Spectrum

Presence of Oscillation – Peak in Spectra

Period and Regularity – Difficult to Judge

detection using acf
Detection using ACF

Time Trend

Power Spectrum

Auto Correlation

Function

Effect of Noise Reduced

ACF oscillates at same frequency as signal

Regularity of oscillations – Zero Crossings of ACF

detection using acf1
Detection using ACF

ACF

Zero

Crossings

Period of Oscillation

Oscillation regular if

clustering using acf
Clustering using ACF

Two signals – same frequency oscillation if

Oscillation considered significant if

(Power in selected band)/(Power in entire spectrum) >

Ref: Thornhill et al., JPC, 2003

multiple oscillations
Multiple Oscillations

Fourier

Transform

Two peaks in Spectra

Use Band pass filters

Calculate ACF for each filtered signal

detection algorithm

Detect and cluster oscillations

Narrow ranges of band pass filters around detected oscillations

Detection Algorithm

Remove Non-stationary trends

Repeat if more than one oscillations present in every filter range OR stop

detection results
Detection: Results

Low 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

low frequency detections

Heat

Exchanger

Chain

Column 2

FC

4

FC

3

LC

10

LC

9

LC

8

LC

7

FC

8

TC

2

FC

7

FC

5

FC

6

LI

5

LI

4

LI

1

LC

3

LC

6

LC

11

PC

1

FI

2

LC

2

AC

2

TI

1

TI

4

TI

5

PC

4

PC

5

PC

2

TC

1

PC

3

LC

1

LC

5

LI

3

LC

4

LI

2

FC

1

FI

1

Compressor

SI

1

FC

2

FI

3

AC

1

Column 1

TI

3

TI

6

TI

2

FI

4

Low frequency detections

158 samples/cycle

137 samples/cycle

OP

PV

PV

OP

summary of detection
Summary 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
possible reasons
Possible Reasons
  • Poorly tuned Controller
  • External disturbances
  • Process induced oscillations
  • Valve Problems
  • MPC model mismatch
definition of stiction
Definition of Stiction

stickband + deadband

E

F

G

moving phase

D

slip jump, j

B

A

C

deadband

stickband

s

valve output (mv)

valve input (op)

test of nonlinearity
Test of Nonlinearity

Central Idea:

Nonlinear interactions between different frequencies

Bispectrum

DFT

Normalized Bispectrum – squaredBicoherence

test of non linearity cont d
Test of Non-linearity (cont’d)

NGI>0 , NLI=0

NGI>0, NLI>0

NGI <= 0

Non-Gaussianity Index and Nonlinearity Index

Critical Values of bic2crit is determined at 95% or 99% confidence interval of the squared bicoherence

Gaussian

Linear

Non-Gaussian

Linear

Non-Gaussian

Nonlinear

Frequency independent

Frequency dependent

flow control loop in a refinery
Flow Control Loop in a Refinery

Assumptions:

  • The process is locally linear in the current operating region
  • Disturbances entering the loop are linear

Loop is Nonlinear

NGI = 0.02 and NLI = 0.55

pattern of stiction in pv op plot
Pattern of Stiction in PV-OP Plot

apparent stiction = maximum width of the cycles in pv-op plot

PV

PV

OP

OP

quantification of apparent stiction
Quantification of Apparent Stiction

4

x 10

1.145

1.14

1.135

1.13

a

b

1.125

PV

P

Q

1.12

1.115

1.11

1.105

38.1

38.2

38.3

38.4

38.5

38.6

38.7

38.8

38.9

OP

Apparent Stiction

= 0.35 %

stiction quantification
Stiction Quantification

FC5

PC1

TC2

No Stiction

0.5%

1.25%

research directions
Research 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
acknowledgements
Acknowledgements
  • NSERC
  • Dr. Nina Thornhill, UK
  • Ebara San, Amano San,

Oonodera San

  • Computer Process Control group