Method and system of using inferential measurements for abnormal event detection in continuous industrial processes

• US 20080281557A1
• Filed: 02/15/2008
• Published: 11/13/2008
• Est. Priority Date: 02/27/2007
• Status: Active Grant

• Current Assignee: ExxonMobil Research & Engineering Company (Exxon Mobil Corporation)
• Original Assignee: ExxonMobil Research & Engineering Company (Exxon Mobil Corporation)
• Inventors: Emigholz, Kenneth F.
• Granted Patent: US 8,285,513 B2
• Time in Patent Office: Days
• US Class Current: 702/179
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• CPC Class Codes: G05B 23/0281 Quantitative, e.g. mathematical distance; Clustering; Neural networks; Statistical analysis

Abstract

The present invention is a method and system for detecting an abnormal on-line analysis or laboratory measurement and for predicting an abnormal quality excursion due to an abnormal process condition.

Claims

1. An on-line system for calculating inferential estimates for abnormal detection of operating parameters measured by an on-line analyzer or laboratory analysis comprising:

a) real-time data collector,

b) real-time data preprocessor,

c) model and statistical significance calculator including an inferential model,

d) means for summarizing multiple model results, and

e) diagnostic displays for event analysis.

2. The system of claim 1 wherein said data collector measures process measurements as inputs to the model.

3. The system of claim 2 wherein said process measurements include temperature, pressure, and flow rates.

4. The system of claim 1 wherein said data preprocessor includes a gross error detection of the data.

5. The system of claim 1 wherein data preprocessor includes mathematical transformation of the data.

6. The system of claim 1 wherein said data preprocessor includes a filter to remove noise.

7. The system of claim 1 wherein said model and statistical significance calculator determines the current operating parameter (Y_estimate), the future predicted operating parameter (Y_predicted), and long-term drift problems in either the model and/or raw measurements.

8. The system of claim 1 wherein said model and statistical significance calculator determines an estimate of the value of the operating parameter.

9. The system of claim 2 wherein said model determines the predicted future value of the final steady state value of the operating parameter if all the inputs remain at their current level (Y_predict).

10. The system of claim 7 wherein said model and statistical significance calculator further determines the future operating parameter for process control application (Y_control).

11. The system of claim 7 wherein inputs to the model for Y_estimate are time synchronized with the corresponding lab analysis or on-line analysis.

12. The system of claim 1 wherein said inferential model can be built using Kalman Filters, partial least squares, regression, neural nets and stepwise regression.

13. The system of claim 11 wherein said inferential model is a partial least squares model.

14. The system of claim 11 wherein said model includes a bias term.

15. The system of claim 14 wherein said bias term is updated whenever an on-line analysis or lab analysis of operating parameters is available.

16. The system of claim 8 wherein said statistical significance calculator determines whether a statistically significant difference exists between the measured operating parameter and the estimate of the operating parameter.

17. The system of claim 16 wherein said statistical significance calculator includes a bias term which is updated based on a difference between the measured operating parameter and the model estimate of the operating parameter.

18. The system of claim 9 wherein said model determines future operating limit excursions by comparing the predicted value of the operating parameter to the operating limit.

19. The system of claim 1 wherein said inferential model includes the identification of surrogate measurements for laboratory analyses.

20. The system of claim 1 wherein said inferential model includes the identification of periods of steady operation and the elimination of most steady-state operations.

21. The system of claim 1 wherein said inferential model includes identification of normal ranges for inputs and outputs measurement.

22. The system of claim 1 wherein said inferential model includes data filters to exclude data during times of very unusual operations.

23. The system of claim 1 wherein said inferential model includes single-input, single-output models.

24. The system of claim 1 wherein said inferential model includes the removal of slow trends from the data.