Multivariate statistical process analysis systems and methods for the production of melt polycarbonate

• US 6,549,864 B1
• Filed: 08/13/2001
• Issued: 04/15/2003
• Est. Priority Date: 08/13/2001
• Status: Expired due to Fees

• Current Assignee: Sabic Innovative Plastics IP BV (Government of Saudi Arabia)
• Original Assignee: General Electric Company
• Inventors: Potyrailo, Radislav Alexandrovich
• Primary Examiner(s): Shah, Kamini
• Assistant Examiner(s): SUN, XIUQIN
• Application Number:
  US09/682,273
• Publication Number:
  US 20030065462A1
• Time in Patent Office: 610 Days
• Field of Search: 700/108, 264/210.2, 702/182, 702/8
• US Class Current: 702/81
• CPC Class Codes: G05B 23/0245 based on a qualitative model, e.g. rule based; if-then decisions
• CPC Class Codes: G06F 18/2135 based on approximation criteria, e.g. principal component analysis

Abstract

Computerized process control systems and methods for the production of melt polycarbonate include a plurality of sensors for obtaining a plurality of measurements relating to a plurality of predetermined process variables, a preprocessor for preprocessing each of the plurality of measurements for multivariate statistical analysis, an identifier for identifying which of the plurality of predetermined process variables affect each of a plurality of predetermined product variables, a correlator for correlating the plurality of predetermined process variables and the plurality of predetermined product variables, and a model generator for modeling the relationship between the plurality of predetermined process variables and the plurality of predetermined product variables. The plurality of predetermined process variables are analyzed to predict polymer performance and/or to ensure that the value of each of the plurality of predetermined process variables is within a predetermined range.

Claims

1. A computerized system for the production of melt polycarbonate, comprising:

a plurality of sensors operable for obtaining a plurality of measurements relating to a plurality of predetermined process variables;

wherein the plurality of predetermined process variables comprise process variables selected from the group consisting of molar ratio, adjusted molar ratio, catalyst concentration, flow rate, vacuum, temperature, torque, discharge pressure, melt viscosity, stirring speed, throughput, and pellet speed;

a preprocessor operable for preprocessing each of the plurality of measurements for multivariate statistical analysis;

an identifier operable for identifying which of the plurality of predetermined process variables affect each of a plurality of predetermined product variables, individually and in combination;

a correlator operable for correlating the plurality of predetermined process variables and the plurality of predetermined product variables, individually and in combination;

a model generator operable for modeling the relationship between the plurality of predetermined process variables and the plurality of predetermined product variables; and

an analyzer operable for analyzing the plurality of predetermined process variables to ensure that the value of each of the plurality of predetermined process variables is within a predetermined range.

2. The system of claim 1, wherein the preprocessor further comprises an algorithm for scaling each of the plurality of measurements.

3. The system of claim 1, wherein the identifier further comprises an algorithm for performing pattern recognition analysis.

4. The system of claim 1, wherein the identifier further comprises an algorithm for performing principal components analysis (PCA).

5. The system of claim 1, wherein the correlator further comprises an algorithm for performing correlation analysis.

6. The system of claim 1, wherein the model generator further comprises an algorithm for performing multivariate calibration.

7. The system of claim 1, wherein the model generator further comprises an algorithm for performing partial least-squares (PLS) regression.

8. The system of claim 1, wherein the analyzer further comprises an algorithm for formulating and analyzing multivariate control charts.

9. The system of claim 1, wherein the plurality of predetermined product variables comprise product variables selected from the group consisting of Fries concentration, melt flow ratio (MFR), pellet intrinsic viscosity (IV), end cap (EC), yellowness index (YI), and melt polycarbonate grade.

10. A computerized system for the production of melt polycarbonate, comprising:

a plurality of sensors operable for obtaining a plurality of measurements relating to a plurality of predetermined process variables;

wherein the plurality of predetermined process variables comprise process variables selected from the group consisting of molar ratio, adjusted molar ratio, catalyst concentration, flow rate, vacuum, temperature, torque, discharge pressure, melt viscosity, stirring speed, throughput, and pellet speed;

a preprocessor having a scaling algorithm, operable for preprocessing each of the plurality of measurements for multivariate statistical analysis;

an identifier having a pattern recognition analysis algorithm, operable for identifying which of the plurality of predetermined process variables affect each of a plurality of predetermined product variables, individually and in combination;

a correlator having a correlation analysis algorithm, operable for correlating the plurality of predetermined process variables and the plurality of predetermined product variables, individually and in combination;

a model generator having a multivariate calibration algorithm, operable for modeling the relationship between the plurality of predetermined process variables and the plurality of predetermined product variables; and

an analyzer having a multivariate control chart algorithm, operable for analyzing the plurality of predetermined process variables to ensure that the value of each of the plurality of predetermined process variables is within a predetermined range.

11. The system of claim 10, wherein the identifier further comprises an algorithm for performing principal components analysis (PCA).

12. The system of claim 10, wherein the model generator further comprises an algorithm for performing partial least-squares (PLS) regression.

13. The system of claim 10, wherein the plurality of predetermined product variables comprise product variables selected from the group consisting of Fries concentration, melt flow ratio (MFR), pellet intrinsic viscosity (IV), end cap (EC), yellowness index (YI), and melt polycarbonate grade.

14. A computerized method for the production of melt polycarbonate, comprising the steps of:

obtaining a plurality of measurements relating to a plurality of predetermined process variables;

wherein the plurality of predetermined process variables comprise process variables selected from the group consisting of molar ratio, adjusted molar ratio, catalyst concentration, flow rate, vacuum, temperature, torque, discharge pressure, melt viscosity, stirring speed, throughput, and pellet speed;

preprocessing each of the plurality of measurements for multivariate statistical analysis;

identifying which of the plurality of predetermined process variables affect each of a plurality of predetermined product variables, individually and in combination;

correlating the plurality of predetermined process variables and the plurality of predetermined product variables, individually and in combination;

modeling the relationship between the plurality of predetermined process variables and the plurality of predetermined product variables; and

analyzing the plurality of predetermined process variables to ensure that the value of each of the plurality of predetermined process variables is within a predetermined range.

15. The method of claim 14, wherein the step of preprocessing each of the plurality of measurements for multivariate statistical analysis further comprises scaling each of the plurality of measurements.

16. The method of claim 14, wherein the step of identifying which of the plurality of predetermined process variables affect each of the plurality of predetermined product variables further comprises performing principal components analysis (PCA).

17. The method of claim 14, wherein the step of correlating the plurality of predetermined process variables and the plurality of predetermined product variables further comprises performing correlation analysis.

18. The method of claim 14, wherein the step of modeling the relationship between the plurality of predetermined process variables and the plurality of predetermined product variables further comprises performing partial least-squares (PLS) regression.

19. The method of claim 14, wherein the step of analyzing the plurality of predetermined process variables to ensure that the value of each of the plurality of predetermined process variables is within a predetermined range further comprises formulating and analyzing multivariate control charts.

20. The method of claim 14, wherein the plurality of predetermined product variables comprise product variables selected from the group consisting of Fries concentration, melt flow ratio (MFR), pellet intrinsic viscosity (IV), end cap (EC), yellowness index (YI), and melt polycarbonate grade.

21. A computerized method for controlling the process for the production of melt polycarbonate, the method comprising the steps of:

measuring the process for the production of melt polycarbonate with a plurality of sensors, the plurality of sensors operable for collecting data representative of a plurality of predetermined process variables and a plurality of predetermined product variables;

wherein the plurality of predetermined process variables comprise process variables selected from the group consisting of molar ratio, adjusted molar ratio, catalyst concentration, flow rate, vacuum, temperature, torque, discharge pressure, melt viscosity, stirring speed, throughput, and pellet speed;

generating a surrogate variable, using a computer in communication with the plurality of sensors, the computer having software operable for performing multivariate statistical analysis, the surrogate variable having a value representative of a control state of the process for the production of melt polycarbonate, the surrogate variable further being a function of a plurality of intermediate variables, each of the plurality of intermediate variables being a weighted function of the plurality of process variables and the plurality of product variables;

determining which of the plurality of intermediate variables primarily contribute to the value of the surrogate variable when the value of the surrogate variable is outside of a predetermined limit;

identifying which of the plurality of process variables primarily contribute to the value of each of the plurality of intermediate variables; and

modifying the process for the production of melt polycarbonate to change each of the plurality of process variables such that the value of the surrogate variable is brought within the predetermined limit.

22. The method of claim 21, wherein measuring the process for the production of melt polycarbonate with a plurality of sensors further comprises scaling each of the plurality of measurements.

23. The method of claim 21, wherein generating a surrogate variable further comprises performing pattern recognition analysis.

24. The method of claim 21, wherein generating a surrogate variable further comprises performing principal components analysis (PCA).

25. The method of claim 21, wherein determining which of the plurality of intermediate variables primarily contribute to the value of the surrogate variable when the value of the surrogate variable is outside of a predetermined limit further comprises performing correlation analysis.

26. The method of claim 21, wherein determining which of the plurality of intermediate variables primarily contribute to the value of the surrogate variable when the value of the surrogate variable is outside of a predetermined limit further comprises performing multivariate calibration.

27. The method of claim 21, wherein identifying which of the plurality of process variables primarily contribute to the value of each of the plurality of intermediate variables further comprises performing partial least-squares (PLS) regression.

28. The method of claim 21, wherein modifying the process for the production of melt polycarbonate to change each of the plurality of process variables such that the value of the surrogate variable is brought within the predetermined limit further comprises formulating and analyzing multivariate control charts.

29. The method of claim 21, wherein the plurality of predetermined product variables comprise product variables selected from the group consisting of Fries concentration, melt flow ratio (MFR), pellet intrinsic viscosity (IV), end cap (EC), yellowness index (YI), and melt polycarbonate grade.