online detection of shutdown periods in chemical plants: a case study

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.



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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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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Data Preparation

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

Data Preparation


Collected every minute within the period from May 2010 to November 2012 (1,268,582 instances).

Data Preparation


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


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.






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.


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


T=inf {t : st (X t)⩾τ }

time input data

detection threshold

statisticshutdownchange-point

T=inf {t : st (X t)<τ }

startupchange-point


Incremental Sliding window

st (X0… t) st(X t−r…t)

Memory requirements:


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

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DATA

2000 2200 2400 2600 2800 3000 3200 34000

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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


44 change points in totalDataset split: 50% train, 50% test


Evaluation


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.


Results

1800 2000 2200 2400 2600 2800 3000 3200 3400-4

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DATA

t

1800 2000 2200 2400 2600 2800 3000 3200 34000

500

1000XS1

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1800 2000 2200 2400 2600 2800 3000 3200 34000

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t

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40MEI

t

st

1800 2000 2200 2400 2600 2800 3000 3200 34000

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40TV

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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

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Window s izeD

elay

Startups ' me dian de lay

TVMEIXS1XS2SGZ

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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

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Window s izeD

elay


TVMEIXS1XS2SGZ

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Window s ize

Del

ay


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

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Window s izeD

elay


TVMEIXS1XS2SGZ

20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 1000

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Window s ize

Del

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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

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Window s izeD

elay


TVMEIXS1XS2SGZ

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Window s ize

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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

[email protected]

online detection of shutdown periods in chemical plants: a case study

Data & Analytics