US 20030065462 A1 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(33) 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; 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 3. The system of 4. The system of 5. The system of 6. The system of 7. The system of 8. The system of 9. The system of 10. The system of 11. 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; 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. 12. The system of 13. The system of 14. The system of 15. The system of 16. 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; 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. 17. The method of 18. The method of 19. The method of 20. The method of 21. The method of 22. The method of 23. The method of 24. 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; 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. 25. The method of 26. The method of 27. The method of 28. The method of 29. The method of 30. The method of 31. The method of 32. The method of 33. The method of Description [0001] The present invention is directed to systems and methods for the control of chemical manufacturing processes and, more specifically, to multivariate statistical process analysis systems and methods for the production of melt polycarbonate. [0002] Manufacturing process variables (X [0003] As the number of monitored variables X [0004] An alternative approach is to employ multivariate statistical process analysis (MSPA) methods to extract more relevant information from measured data. MSPA methods provide the staff of a manufacturing plant, for example, with a greater understanding of process performance, allowing them to make sound business decisions. Thus, the application of multivariate methodologies to industrial manufacturing processes has experienced increasing popularity in recent years. For example, MSPA methods have been utilized in emulsion polymerization, low-density continuous polyethylene polymerization, batch polymerization, and pilot-scale penicillin fermentation processes. Similarly, MSPA methods have been utilized to improve the productivity of a titanium dioxide plant, monitor the processing conditions of a nuclear waste storage tank, and control the performance of chromatographic instrumentation. [0005] The application of multivariate statistical analysis methods to industrial process data characterized by a large number of correlated chemical process measurements is the area of process chemometrics. The objectives of process chemometrics include the determination of key process variables, the generation of inference models used to forecast and optimize product quality, the detection and diagnosis of faults and potential process abnormalities, and the overall monitoring of chemical processes to ensure production control. Achieving these goals is often difficult with regard to the production of melt polycarbonate, however, as the determination of key process variables may be an inexact and time consuming process, and accurate and reliable inference models may be difficult to generate. [0006] Thus, the present invention is directed to automated multivariate statistical process analysis systems and methods for the production of melt polycarbonate. [0007] These systems and methods allow process variables causing abnormal performance to be detected and identified. As a result, a manufacturing plant staff may better understand process performance and make sound business decisions. [0008] In one embodiment, a computerized system for the production of melt polycarbonate includes 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, a model generator for modeling the relationship between the plurality of predetermined process variables and the plurality of predetermined product variables, and an analyzer for analyzing the plurality of predetermined process variables 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. [0009] In another embodiment, a computerized method for the production of melt polycarbonate includes the steps of obtaining a plurality of measurements relating to a plurality of predetermined process variables, 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, correlating the plurality of predetermined process variables and the plurality of predetermined product variables, 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 predict polymer performance and/or to ensure that the value of each of the plurality of predetermined process variables is within a predetermined range. [0010]FIG. 1 is a flow chart of a computerized multivariate statistical process analysis method for the production of melt polycarbonate; [0011]FIG. 2 is a plot of the eigenvalues of process variables, measured using the method of FIG. 1, after autoscaling; [0012]FIG. 3 is a graph of the percent of captured variance for each process variable for the first two principal components (PCs) of the principal components analysis (PCA) model of the present invention; [0013]FIG. 4 is a loadings plot from the PCA model of FIG. 3; [0014]FIG. 5 is a loadings plot for the first PC from the PCA model of FIG. 3; [0015]FIG. 6 is a loadings plot for the fourth PC from the PCA model of FIG. 3; [0016]FIG. 7 is a plot of the first two scores for the PCA model of FIG. 3; [0017]FIG. 8 is a plot of the result of the prediction of pellet intrinsic viscosity (IV) using only process variables; [0018]FIG. 9 is a plot of the result of the prediction of Fries concentration using only process variables; [0019]FIG. 10 is a Q control chart for the multivariate statistical process analysis of the production of melt polycarbonate resin; [0020]FIG. 11 is a T [0021]FIG. 12 is a functional block diagram of a computerized multivariate statistical process analysis system for the production of melt polycarbonate. [0022] Polycarbonates are typically prepared from dihydric phenol compounds and carbonic acid derivatives. For example, one important polycarbonate, melt polycarbonate, may be prepared via the melt polymerization of diphenyl carbonate and Bisphenol A (BPA). The reaction is conducted at high temperatures, allowing the starting monomers and product to remain molten while the reactor pressure is staged in order to more effectively remove phenol, the by-product of the polycondensation reaction. [0023] During the melt polycarbonate manufacturing process, data may be collected via sensors in order to monitor process performance. Using this collected information, the relative importance of various process variables (X [0024] Referring to FIG. 1, a multivariate statistical process analysis (MSPA) method
[0025] In the above table, “R [0026] The variables used for multivariate analysis are further described in Table 2.
[0027] Prior to multivariate analysis, gathered data may be preprocessed [0028] where X [0029] and σ [0030] Following the data preprocessing step [0031] where t [0032] To determine the number of principal components to retain in the PCA model, the percent variance captured by the PCA model may be analyzed (see Table 3 below) in combination with a plot of eigenvalues as a function of PCs
[0033] Information regarding the amount of variance for each process variable X [0034] Referring to FIG. 4, a loadings plot [0035] It is also important to note the amount of variation described by a PC when interpreting loadings. A variable with a large loading value contributes significantly to a particular PC. However, the variable may not be truly important if the PC does not describe a large amount of the variation in the data set. FIG. 5 presents the values of loadings of process variables X [0036] Another step in the multivariate statistical process analysis method [0037] where R is the correlation coefficient and N is the number of data points. The correlation coefficient R is between −1 and 1 and is independent of the scale of x and y values. For an exact linear relation between x and y, R=−1 if increasing x values correspond to increasing y values and R=−1 if increasing x values correspond to decreasing y values. R=0 if the variables are independent. [0038] Results of the correlation analysis of process variables X [0039] In the example discussed above, two pairs of process variables X [0040] Initial analysis of the correlation structure in the combined data set of process variables X [0041] A detailed analysis of the correlation between process variables X [0042] A more in-depth understanding of the relationships between process variables X [0043] Referring again to FIG. 1, the multivariate statistical process analysis method [0044] In the example discussed above, the results of the prediction of pellet IV and Fries concentration using only process variables X
[0045] To ensure normal manufacturing plant operation, the quality of collected process variables X [0046] where e [0047] where t [0048] Referring to FIGS. 10 and 11, the Q and T [0049] Referring to FIG. 12, a multivariate statistical process analysis (MSPA) system [0050] Structurally, the computer [0051] The computer's memory preferably contains a number of programs or algorithms for functionally controlling the operation of the system [0052] The present invention has been described with reference to examples and preferred embodiments. Other examples and embodiments may achieve the same results. Variations in and modifications to the present invention will be apparent to those skilled in the art and the following claims are intended to cover all such equivalents. Referenced by
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