Online Detection of Shutdown Periods in Chemical Plants: A Case Study
Manuel Martín Salvadora, Bogdan Gabrysa, Indrė Žliobaitėb
aFaculty of Science and Technology, Bournemouth University, United KingdombDept. of Information and Computer Science, Aalto University, Finland
KES2014, Gdynia, PolandBackground picture is Creative Commons by Paul Joyce
Outline
1. INFER Project2. Motivation3. Data Preparation4. Shutdown Identification
4.1. What is a shutdown period?4.2. Problems and solutions4.3. Shutdown and startup phases4.4. Multi-sensor change-point detection methods4.5. Our method
5. Evaluation6. Results7. Conclusion
MotivationMotivation
Company goal: To improve the production of acrylic acid.
Acrylic acid molecule (C3H4O2) is used for plastics, coatings, adhesives, elastomers, floor polishes and paints.
MotivationMotivation
Company goal: To improve the production of acrylic acid.
Initial status: Process monitoring is carried out by human operators to control the production.
Concentration of acrylic acid is measured in the laboratory by taking samples every 4 hours.
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Acrylic acid molecule (C3H4O2) is used for plastics, coatings, adhesives, elastomers, floor polishes and paints.
MotivationMotivation
Company goal: To improve the production of acrylic acid.
Initial status: Process monitoring is carried out by human operators to control the production.
Concentration of acrylic acid is measured in the laboratory by taking samples every 4 hours.
Research goal: To build a soft sensor for predicting acrylic acid concentration every minute.
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Acrylic acid molecule (C3H4O2) is used for plastics, coatings, adhesives, elastomers, floor polishes and paints.
Data Preparation
The chemical plant contains hundreds of sensors, but only 53 of them were selected with the help of experts.
Data Preparation
The chemical plant contains hundreds of sensors, but only 53 of them were selected with the help of experts.
Collected every minute within the period from May 2010 to November 2012 (1,268,582 instances).
Data Preparation
The chemical plant contains hundreds of sensors, but only 53 of them were selected with the help of experts.
Collected every minute within the period from May 2010 to November 2012 (1,268,582 instances).
● Target back-shifting● Handling of missing values● Shutdown identification● Detecting and handling outliers● Steady state identification● Finding variable delays and synchronization● Adding new variables
Data pre-processing tasks:
Shutdown Identification
Task: To robustly and accurately identify the shutdown periods even in case of multiple sensor failures.
Why? To avoid the updating of soft sensors with irrelevant data.
What is a shutdown period?
A shutdown period is an undefined period of time in which the production plant is stopped.
Problem: There is no single variable indicating on/off.
Problems and solutions
Problem: There is no single variable indicating on/off.
Solution: Monitor a sensor and detect abrupt changes.
Problems and solutions
Problem: There is no single variable indicating on/off.
Solution: Monitor a sensor and detect abrupt changes.
Problem: A single sensor may fail.
Problems and solutions
Problem: There is no single variable indicating on/off.
Solution: Monitor a sensor and detect abrupt changes.
Problem: A single sensor may fail.
Solution: Monitor multiple sensors at the same time.
Problems and solutions
Problem: There is no single variable indicating on/off.
Solution: Monitor a sensor and detect abrupt changes.
Problem: A single sensor may fail.
Solution: Monitor multiple sensors at the same time.
Problem: There are delays between sensors due to physical location in the plant.
Problems and solutions
Problem: There is no single variable indicating on/off.
Solution: Monitor a sensor and detect abrupt changes.
Problem: A single sensor may fail.
Solution: Monitor multiple sensors at the same time.
Problem: There are delays between sensors due to physical location in the plant.
Solution: Synchronize variables (not easy) or use right change-point detection methods.
Problems and solutions
Shutdown and startup phases
Only 11 flow sensors were selected because they are the most responsive.
Multi-Sensor Change-Point Detection Methods
T=inf {t : st (X t)⩾τ}
time input data
detection threshold
statisticshutdownchange-point
Multi-Sensor Change-Point Detection Methods
T=inf {t : st (X t)⩾τ }
time input data
detection threshold
statisticshutdownchange-point
T=inf {t : st (X t)<τ }
startupchange-point
Multi-Sensor Change-Point Detection Methods
Incremental Sliding window
st (X0… t) st(X t−r…t)
Memory requirements:
Multi-Sensor Change-Point Detection Methods
Incremental Sliding window
st (X0… t) st(X t−r…t)
Memory requirements:
Sensors relevance:
Fixed Dynamic
st (W X t) st (W t X t)
Our method
binary weight for sensor n
number of outliers in the window
● Sliding window● Dynamic weights● Based on control charts
Thresholds by Hampel identifier:Median ± 3*MAD
Quick detection for shutdowns and deferred detection for
startups
binary weight for sensor n
number of outliers in the window
● Sliding window● Dynamic weights● Based on control charts
Thresholds by Hampel identifier:Median ± 3*MAD
2000 2200 2400 2600 2800 3000 3200 3400
-2
0
2
DATA
2000 2200 2400 2600 2800 3000 3200 34000
10
20
30SGZ
t
st
Our method
Quick detection for shutdowns and deferred detection for
startups
Evaluation
5 Multi-sensor change-point detection methods have been evaluated: 4 based on likelihood and 1 on control charts.
TV: Tartakovsky, A. & Veeravalli, V., 2008. Asymptotically Optimal Quickest Change Detection in Distributed Sensor Systems. Sequential Analysis, 27(4), pp.441–475MEI: Mei, Y., 2010. Efficient scalable schemes for monitoring a large number of data streams. Biometrika, 97(2), pp.419–433XS1, XS2: Xie, Y. & Siegmund, D., 2013. Sequential multi-sensor change-point detection. The Annals of Statistics, 41(2), pp.670–692SGZ: Salvador, M.M., Gabrys, B. & Žliobaitė, I., 2014. Online Detection of Shutdown Periods in Chemical Plants: A Case Study. Procedia Computer Science, 35, pp.580–588
Evaluation
5 Multi-sensor change-point detection methods have been evaluated: 4 based on likelihood and 1 on control charts.
44 change points in totalDataset split: 50% train, 50% test
TV: Tartakovsky, A. & Veeravalli, V., 2008. Asymptotically Optimal Quickest Change Detection in Distributed Sensor Systems. Sequential Analysis, 27(4), pp.441–475MEI: Mei, Y., 2010. Efficient scalable schemes for monitoring a large number of data streams. Biometrika, 97(2), pp.419–433XS1, XS2: Xie, Y. & Siegmund, D., 2013. Sequential multi-sensor change-point detection. The Annals of Statistics, 41(2), pp.670–692SGZ: Salvador, M.M., Gabrys, B. & Žliobaitė, I., 2014. Online Detection of Shutdown Periods in Chemical Plants: A Case Study. Procedia Computer Science, 35, pp.580–588
Evaluation
5 Multi-sensor change-point detection methods have been evaluated: 4 based on likelihood and 1 on control charts.
44 change points in totalDataset split: 50% train, 50% test
Goal: Detect all the change points while minimizing both the (positive) detection delay and the number of false detections.
TV: Tartakovsky, A. & Veeravalli, V., 2008. Asymptotically Optimal Quickest Change Detection in Distributed Sensor Systems. Sequential Analysis, 27(4), pp.441–475MEI: Mei, Y., 2010. Efficient scalable schemes for monitoring a large number of data streams. Biometrika, 97(2), pp.419–433XS1, XS2: Xie, Y. & Siegmund, D., 2013. Sequential multi-sensor change-point detection. The Annals of Statistics, 41(2), pp.670–692SGZ: Salvador, M.M., Gabrys, B. & Žliobaitė, I., 2014. Online Detection of Shutdown Periods in Chemical Plants: A Case Study. Procedia Computer Science, 35, pp.580–588
Results
1800 2000 2200 2400 2600 2800 3000 3200 3400-4
-2
0
2
DATA
t
1800 2000 2200 2400 2600 2800 3000 3200 34000
500
1000XS1
t
st
1800 2000 2200 2400 2600 2800 3000 3200 34000
10
20
30
40XS2
t
st
1800 2000 2200 2400 2600 2800 3000 3200 34000
10
20
30
40MEI
t
st
1800 2000 2200 2400 2600 2800 3000 3200 34000
10
20
30
40TV
t
st
1800 2000 2200 2400 2600 2800 3000 3200 34000
10
20
30SGZ
t
st
Snapshot of the observed data and st values for r=25
Results
20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100-40
-20
0
20
40
60
80
Window s izeD
elay
Startups ' me dian de lay
TVMEIXS1XS2SGZ
20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 1000
2
4
6
8
10
12
14
Window s ize
Del
ay
Shutdowns ' me dian de lay
TVMEIXS1XS2SGZ
Window size doesn't affect too much the shutdown detection. However, it has a considerable impact in the startup detection.
Results
20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100-40
-20
0
20
40
60
80
Window s izeD
elay
Startups ' me dian de lay
TVMEIXS1XS2SGZ
20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 1000
2
4
6
8
10
12
14
Window s ize
Del
ay
Shutdowns ' me dian de lay
TVMEIXS1XS2SGZ
MEI is a quick detector but also raises false alarms
Results
20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100-40
-20
0
20
40
60
80
Window s izeD
elay
Startups ' me dian de lay
TVMEIXS1XS2SGZ
20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 1000
2
4
6
8
10
12
14
Window s ize
Del
ay
Shutdowns ' me dian de lay
TVMEIXS1XS2SGZ
The method that presents lower positive delay both in shutdowns and startups while minimizing the memory requirements (i.e. window size) is XS1.
Results
20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100-40
-20
0
20
40
60
80
Window s izeD
elay
Startups ' me dian de lay
TVMEIXS1XS2SGZ
20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 1000
2
4
6
8
10
12
14
Window s ize
Del
ay
Shutdowns ' me dian de lay
TVMEIXS1XS2SGZ
Our method has a slightly higher detection delay but on the other hand is robust against sensor failures.
Conclusion
State-of-the-art multi-sensor change-point detection methods have been compared in a real case scenario which is novel in the literature.
Shutdown and startups have to be treated differently.
Our method is prepared for sensor failures.
Next step is to study the impact of the shutdown detection on the soft sensor performance.
Thanks!
Slides available in http://slideshare.net/draxus
Source code available in https://github.com/draxus/online-shutdown-identification