In process industry, chemical processes are controlled and monitored by using readings from multiple physical sensors across the plants. Such physical sensors are also supplemented by soft sensors, i.e. adaptive predictive models, which are often used for computing hard-to-measure variables of the process. For soft sensors to work well and adapt to changing operating conditions they need to be provided with relevant data. As production plants are regularly stopped, data instances generated during shutdown periods have to be identified to avoid updating these predictive models with wrong data. We present a case study concerned with a large chemical plant operation over a 2 years period. The task is to robustly and accurately identify the shutdown periods even in case of multiple sensor failures. State-of-the-art methods were evaluated using the first half of the dataset for calibration purposes and the other half for measuring the performance. Results show that shutdowns (i.e. sudden changes) can be quickly detected in any case but the detection delay of startups (i.e. gradual changes) is directly related with the choice of a window size.

1. KES2014, Gdynia, Poland
Background picture is Creative Commons by Paul Joyce
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 Kingdom
bDept. of Information and Computer Science, Aalto University, Finland

2. Outline
1. INFER Project
2. Motivation
3. Data Preparation
4. Shutdown Identification
4.1. What is a shutdown period?
4.2. Problems and solutions
4.3. Shutdown and startup phases
4.4. Multi-sensor change-point detection methods
4.5. Our method
5. Evaluation
6. Results
7. Conclusion

4. MMoottiivvaattiioonn
Company goal: To improve
the production of acrylic acid.
Acrylic acid
molecule (C3H4O2)
is used for plastics,
coatings,
adhesives,
elastomers, floor
polishes and paints.

5. MMoottiivvaattiioonn
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.
Acrylic acid
molecule (C3H4O2)
is used for plastics,
coatings,
adhesives,
elastomers, floor
polishes and paints.
Picture is Creative Commons by Jm3

6. MMoottiivvaattiioonn
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.
Acrylic acid
molecule (C3H4O2)
is used for plastics,
coatings,
adhesives,
elastomers, floor
polishes and paints.
Picture is Creative Commons by Jm3

7. Data Preparation
The chemical plant contains hundreds of
sensors, but only 53 of them were selected
with the help of experts.

8. 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).

9. 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 pre-processing tasks:
● Target back-shifting
● Handling of missing values
● Shutdown identification
● Detecting and handling outliers
● Steady state identification
● Finding variable delays and synchronization
● Adding new variables

10. 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.

11. What is a shutdown period?
A shutdown period is an undefined period of
time in which the production plant is stopped.

12. Problems and solutions
Problem: There is no single variable indicating on/off.

13. Problems and solutions
Problem: There is no single variable indicating on/off.
Solution: Monitor a sensor and detect abrupt changes.

14. 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.

15. 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.

16. 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.

17. 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.

18. Shutdown and startup phases
Only 11 flow sensors were selected because they are the most responsive.

19. Multi-Sensor Change-Point
Detection Methods
T=inf {t : st (Xt )⩾τ }
time input
data
detection
threshold
shutdown statistic
change-point

20. Multi-Sensor Change-Point
Detection Methods
T=inf {t : st (Xt)⩾τ }
time input
data
detection
threshold
shutdown statistic
change-point
T=inf {t : st (Xt )<τ }
startup
change-point

21. Multi-Sensor Change-Point
Detection Methods
Memory requirements:
Incremental Sliding window
st (X0…t) st (Xt−r…t)

22. Multi-Sensor Change-Point
Detection Methods
Memory requirements:
Incremental Sliding window
st (X0…t) st (Xt−r…t)
Sensors relevance:
Fixed Dynamic
st (W Xt) st (Wt Xt )

23. 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

24. binary
weight for
sensor n
2000 2200 2400 2600 2800 3000 3200 3400
2
0
-2
30
20
10
number of outliers
in the window
● Sliding window
● Dynamic weights
● Based on control charts
Thresholds by Hampel identifier:
Median ± 3*MAD
DATA
2000 2200 2400 2600 2800 3000 3200 3400
0
SGZ
t
s
t
Our method
Quick detection
for shutdowns
and deferred
detection for
startups

25. 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.441–475
MEI: Mei, Y., 2010. Efficient scalable schemes for monitoring a large number of data
streams. Biometrika, 97(2), pp.419–433
XS1, XS2: Xie, Y. & Siegmund, D., 2013. Sequential multi-sensor change-point detection.
The Annals of Statistics, 41(2), pp.670–692
SGZ: 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

26. 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.441–475
MEI: Mei, Y., 2010. Efficient scalable schemes for monitoring a large number of data
streams. Biometrika, 97(2), pp.419–433
XS1, XS2: Xie, Y. & Siegmund, D., 2013. Sequential multi-sensor change-point detection.
The Annals of Statistics, 41(2), pp.670–692
SGZ: 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
44 change points in total
Dataset split: 50% train, 50% test

27. 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.441–475
MEI: Mei, Y., 2010. Efficient scalable schemes for monitoring a large number of data
streams. Biometrika, 97(2), pp.419–433
XS1, XS2: Xie, Y. & Siegmund, D., 2013. Sequential multi-sensor change-point detection.
The Annals of Statistics, 41(2), pp.670–692
SGZ: 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
44 change points in total
Dataset split: 50% train, 50% test
Goal: Detect all the change points while
minimizing both the (positive) detection
delay and the number of false detections.

28. Results
1800 2000 2200 2400 2600 2800 3000 3200 3400
2
0
-2
-4
DATA
t
1800 2000 2200 2400 2600 2800 3000 3200 3400
40
30
20
10
1000
500
0
XS1
t
st
1800 2000 2200 2400 2600 2800 3000 3200 3400
30
20
10
40
30
20
10
40
30
20
10
0
XS2
t
st
1800 2000 2200 2400 2600 2800 3000 3200 3400
0
MEI
t
st
1800 2000 2200 2400 2600 2800 3000 3200 3400
0
TV
t
st
1800 2000 2200 2400 2600 2800 3000 3200 3400
0
SGZ
t
st
Snapshot of the observed data and st values for r=25

29. Results
20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
80
60
40
20
0
-20
-40
Window s ize
De lay
Startups ' me dian de lay
TV
MEI
XS1
XS2
SGZ
20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
14
12
10
8
6
4
2
0
Window s ize
Delay
Shutdowns ' me dian de lay
TV
MEI
XS1
XS2
SGZ
Window size doesn't affect too much the shutdown
detection. However, it has a considerable impact in
the startup detection.

30. Results
20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
80
60
40
20
0
-20
-40
Window s ize
De lay
Startups ' me dian de lay
TV
MEI
XS1
XS2
SGZ
20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
14
12
10
8
6
4
2
0
Window s ize
Delay
Shutdowns ' me dian de lay
TV
MEI
XS1
XS2
SGZ
MEI is a quick detector but also raises false alarms

31. Results
20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
80
60
40
20
0
-20
-40
Window s ize
De lay
Startups ' me dian de lay
TV
MEI
XS1
XS2
SGZ
20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
14
12
10
8
6
4
2
0
Window s ize
Delay
Shutdowns ' me dian de lay
TV
MEI
XS1
XS2
SGZ
The method that presents lower positive delay both in
shutdowns and startups while minimizing the memory
requirements (i.e. window size) is XS1.

32. Results
20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
80
60
40
20
0
-20
-40
Window s ize
De lay
Startups ' me dian de lay
TV
MEI
XS1
XS2
SGZ
20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
14
12
10
8
6
4
2
0
Window s ize
Delay
Shutdowns ' me dian de lay
TV
MEI
XS1
XS2
SGZ
Our method has a slightly higher detection delay but
on the other hand is robust against sensor failures.

33. 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.

34. Thanks!
Slides available in http://slideshare.net/draxus
Source code available in
https://github.com/draxus/online-shutdown-identification
msalvador@bournemouth.ac.uk