Method for identifying unmeasured disturbances in process control test data

• US 20090076773A1
• Filed: 09/14/2007
• Published: 03/19/2009
• Est. Priority Date: 09/14/2007
• Status: Abandoned Application

• Current Assignee: Texas Tech University (Texas Tech University System)
• Original Assignee: Texas Tech University (Texas Tech University System)
• Inventors: Dong, Hongbin, Riggs, James B.
• Application Number:
  US11/855,327
• Publication Number:
  US 20090076773A1
• Time in Patent Office: Days
• US Class Current: 702/179
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G05B 17/02 electric

Abstract

A method of improving a data set associated with a system, the method comprising: providing a baseline data set for the system, wherein the baseline data set comprises a plurality of input and output data from the system; analyzing the baseline data set and selecting a baseline model for the baseline data set, wherein the baseline model comprises an baseline array of data relating to the input and output data from the system; normalizing the baseline data set associated with the system to create a normalized data set; analyzing the normalized data set and selecting an improved model for the normalized data set, wherein the improved model comprises an improved array of data relating to the input and output data from the system; performing a statistical comparison using the baseline data set and the normalized data set; calculating an at least one indicator value associated with the normalized and baseline data set based on the statistical comparison; determining a threshold value associated with the baseline data set and normalized data set based on the statistical comparison; and producing a new data set by eliminating any segments of the baseline data set where the at least one indicator value is greater than the threshold value.

Claims

1. A method of improving a data set associated with a system, the method comprising:

providing a baseline data set for the system, wherein the baseline data set comprises a plurality of input and output data from the system;

analyzing the baseline data set and selecting a baseline model for the baseline data set, wherein the baseline model comprises an baseline array of data relating to the input and output data from the system;

normalizing the baseline data set associated with the system to create a normalized data set;

analyzing the normalized data set and selecting an improved model for the normalized data set, wherein the improved model comprises an improved array of data relating to the input and output data from the system;

performing a statistical comparison using the baseline data set and the normalized data set;

calculating an at least one indicator value associated with the normalized and baseline data set based on the statistical comparison;

determining a threshold value associated with the baseline data set and normalized data set based on the statistical comparison; and

producing a new data set by eliminating any segments of the baseline data set where the at least one indicator value is greater than the threshold value.

2. The method of claim 1 wherein the eliminated data segments are associated with abnormal operating conditions of the system.

3. The method of claim 1 wherein abnormal operating conditions of the system are associated with unmeasured disturbances of the system.

4. The method of claim 1, further comprising:

iteratively repeating the method of claim 1 at least once, wherein the new data set is used as the baseline data set in at least one iteration.

5. The method of claim 1, wherein normalizing the baseline data set comprises linearizing the baseline data set to create the improved data set.

6. The method of claim 1, wherein the at least one indicator value is calculated through the statistical comparison of the baseline data set and the improved data set using a global chi-squared value.

7. The method of claim 1, wherein the at least one indicator value is derived from a false alarm rate.

8. The method of claim 6, wherein the threshold value is an improved chi-squared threshold value.

9. The method of claim 1, wherein the at least one model of the system comprises a model predictive control model, a Finite Impulse Response (FIR) model, a transfer function model, a state-space model, or combination thereof.

10. The method of claim 1, wherein the at least one model of the system is a model predictive control model that further comprises a single-input single-output (SISO) model, a multiple-input multiple-output (MIMO) model, a single-input multiple-output (SIMO) model, a multiple-input single-output (MISO) model, or combinations thereof.

11. The method of claim 5, wherein the identification of the at least one model of the system is based upon the degree of linearization of the baseline data set.

12. The method of claim 1, further comprising simulating operation of the system using the model of the system and the new data set.

13. The method of claim 1, further comprising controlling operation of the system using one or more results of the simulation.

14. The method of claim 13, wherein the controlling operation of the system comprises model predictive control.

15. The method of claim 13, wherein the system is one or more operating units of a petroleum refining and/or chemical manufacturing plant.

16. The method of claim 15, wherein the baseline data set is gathered from one or more test runs of the one or more operating units.

17. A system for identifying unmeasured disturbances in a process comprising:

a data entry unit, wherein the data entry unit accepts a baseline data set associated with the process, selects a baseline model for the baseline data set, normalizes the baseline data set and creates an improved data set from the normalized baseline data set, and selects an improved model for the improved data set;

a modeling unit, wherein the modeling unit calculates a statistical threshold value using the baseline data set and the improved data set calculates an indicator value using the baseline data set and the improved data set, and produces a new data set by eliminating any data in the baseline data set in which the indicator value is greater than the statistical threshold value; and

a data output unit, wherein the data output unit outputs the new data set to a computer readable medium.

18. A process control device capable of implementing model predictive control of a process, the process control device performing a method comprising:

identifying a first model predictive control model using a training data set associated with the process;

identifying a second model predictive control model using a linearized training data set;

statistically comparing the results of the first model predictive control model and the second model predictive control model;

detecting differences in the statistical comparison of the first model predictive control model and the second model predictive control model;

producing a new data set by eliminating any data in the training data set based upon the differences in the first model predictive control model and the second model predictive control model.

19. The process control device of claim 18 further comprising:

iteratively performing the process control device method using the new data as training data set.

20. The process control device of claim 18 wherein the statistical comparison is preformed through a chi-squared function.

21. The process control device of claim 18 wherein the new data is created by removing data from the training data set.

22. A method of identifying unmeasured disturbances in model predictive control test data comprising:

identifying a first model predictive control model using a training data set;

identifying a second model predictive control model using an improved training data set;

calculating a global chi-squared value using the first model predictive control model, the second model predictive control model, the training data set, and the improved training data set;

calculating an improved chi-squared threshold value; and

producing a new data set by eliminating any data in the training data set in which the global chi-squared value is greater than the improved chi-squared threshold value.

23. The method of claim 22 further comprising iteratively repeating the method until all of the global chi-squared values are less than or equal to the improved chi-squared threshold values.

24. The method of claim 22 wherein the first model predictive control model, the second model predictive control model, or both are obtained using least-squares regression method.

25. The method of claim 22 wherein the improved version of the training data set is obtained by piecewise linearizing the training data set.