Online Detection of Shutdown Periods in Chemical Plants: A Case Study

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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.

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Online Detection of Shutdown Periods in Chemical Plants: A Case Study

  1. 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. 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
  3. 3. 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.
  4. 4. 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
  5. 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. 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
  6. 6. Data Preparation The chemical plant contains hundreds of sensors, but only 53 of them were selected with the help of experts.
  7. 7. 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).
  8. 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). 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
  9. 9. 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.
  10. 10. What is a shutdown period? A shutdown period is an undefined period of time in which the production plant is stopped.
  11. 11. Problems and solutions Problem: There is no single variable indicating on/off.
  12. 12. Problems and solutions Problem: There is no single variable indicating on/off. Solution: Monitor a sensor and detect abrupt changes.
  13. 13. 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.
  14. 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. Solution: Monitor multiple sensors at the same time.
  15. 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. Problem: There are delays between sensors due to physical location in the plant.
  16. 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. Solution: Synchronize variables (not easy) or use right change-point detection methods.
  17. 17. Shutdown and startup phases Only 11 flow sensors were selected because they are the most responsive.
  18. 18. Multi-Sensor Change-Point Detection Methods T=inf {t : st (Xt )⩾τ } time input data detection threshold shutdown statistic change-point
  19. 19. 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
  20. 20. Multi-Sensor Change-Point Detection Methods Memory requirements: Incremental Sliding window st (X0…t) st (Xt−r…t)
  21. 21. 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 )
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 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 44 change points in total Dataset split: 50% train, 50% test
  26. 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 Goal: Detect all the change points while minimizing both the (positive) detection delay and the number of false detections.
  27. 27. 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
  28. 28. 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.
  29. 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 MEI is a quick detector but also raises false alarms
  30. 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 The method that presents lower positive delay both in shutdowns and startups while minimizing the memory requirements (i.e. window size) is XS1.
  31. 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 Our method has a slightly higher detection delay but on the other hand is robust against sensor failures.
  32. 32. 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.
  33. 33. Thanks! Slides available in http://slideshare.net/draxus Source code available in https://github.com/draxus/online-shutdown-identification msalvador@bournemouth.ac.uk

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