Multivariate prediction of a batch manufacturing process

• US 9,541,471 B2
• Filed: 01/20/2014
• Issued: 01/10/2017
• Est. Priority Date: 04/06/2012
• Status: Active Grant

• Current Assignee: MKS Instruments AB (Sartorius AG)
• Original Assignee: MKS Instruments Incorporated
• Inventors: McCready, Chris Peter
• Primary Examiner(s): Bui, Bryan
• Application Number:
  US14/158,914
• Publication Number:
  US 20140136146A1
• Time in Patent Office: 1,086 Days
• Field of Search: 702/179
• US Class Current: 1/1
• CPC Class Codes: G01M 99/00 Subject matter not provided for in other groups of this subclass
• 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 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks

Abstract

A method and system for predicting prospective behavior of a manufacturing process are described. Measured values of multiple variables, including at least one dependent variable, are received. A partial least squares (PLS) regression approach is used to estimate an unknown future value of the at least one dependent variable at a future point in time in a current batch run.

Claims

1. A computer-implemented method for predicting prospective behavior of a batch-type manufacturing process with a finite duration, the method comprising:

receiving, by a computing device, measured values of a plurality of variables of the manufacturing process, including (i) measured values of the plurality of variables associated with at least one historical batch run and (ii) measured values of the plurality of variables associated with at least one current batch run representative of values measured up to a current maturity point in time, wherein the plurality of variables comprise at least one dependent variable that represents a process parameter whose value is dependent on one more process conditions; and

using, by the computing device, a partial least squares (PLS) regression approach to estimate an unknown future value of the at least one dependent variable at a future point in time in the at least one current batch run, wherein using the PLS regression approach comprises;

creating, by the computing device, a X matrix including the measured values of the plurality of variables associated with the at least one historical batch run;

creating, by the computing device, a Y matrix including the measured values of the at least one dependent variable associated with the at least one historical batch run;

applying, by the computing device, the PLS regression approach to determine a relationship between the X matrix and the Y matrix, wherein the relationship is represented by a β

matrix; and

using, by the computing device, the β

matrix and the measured values of the plurality of variables for the at least one current batch run to estimate the unknown future value of the at least one dependent variable for the at least one current batch run; and

using, the β

matrix to predict a plurality of future values of the at least one dependent variable at a plurality of future points in time after the current maturity point to the end of the finite duration of the at least one current batch run.

2. The computer-implemented method of claim 1, wherein the at least one dependent variable is not directly assignable during the manufacturing process.

3. The computer-implemented method of claim 1, wherein the plurality of variables of the manufacturing process further include at least one manipulated variable that represents a process parameter whose value is assignable during the manufacturing process.

4. The computer-implemented method of claim 3, further comprising:

receiving at least one future value of the at least one manipulated variable associated with the at least one current batch run; and

using (1) the relationship represented by the β

matrix, (2) the measured values of the plurality of variables for the at least one current batch run, and (3) the at least one future value of the at least one manipulated variable associated with the at least one current batch run to estimate the unknown future value of the at least one dependent variable for the at least one current batch.

5. The computer-implemented method of claim 4, wherein the at least one future value of the at least one manipulated variable represents a known value for setting the at least one manipulated variable at a future point in time in the at least one current batch run.

6. The computer-implemented method of claim 1, further comprising running the PLS regression approach once without iteration to determine the β

matrix.

7. The computer-implemented method of claim 1, wherein the measured values of the at least one dependent variable associated with the at least one historical batch run appears in both the X matrix and the Y matrix.

8. The computer-implemented method of claim 1, further comprising performing multivariate analysis on a combination of (1) the measured values of the plurality of variables and (2) the estimated future value of the at least one dependent variable to generate a plurality of multivariate statistics for the at least one current batch run.

9. The computer-implemented method of claim 8, wherein the plurality of multivariate statistics represent a trajectory of at least one of past, current or estimated future behavior of the manufacturing process.

10. The computer-implemented method of claim 1, wherein the manufacturing process comprises growing a cell culture medium to achieve one or more quality profiles for the cell culture medium.

11. The computer-implemented method of claim 10, wherein the plurality of variables of the manufacturing process comprise a plurality of physical, chemical and biological parameters and wherein the PLS regression approach is used to estimate an unknown future value of at least one dependent variable from the plurality of physical, chemical and biological parameters to achieve the one or more quality profiles.

12. The computer-implemented method of claim 11, wherein the physical parameters include at least one of temperature, gas flow rate or agitation speed.

13. The computer-implemented method of claim 11, wherein the chemical parameters include at least one of dissolved oxygen concentration, carbon dioxide concentration, pH concentration, osmolality, redox potential, metabolite level, amino acid concentration or waste by-products production.

14. The computer-implemented method of claim 11, wherein the biological parameters include at least one of viable cell concentration, intra-cellular measurements or extra-cellular measurements.

15. The computer implemented method of claim 10, wherein the one or more quality profiles comprise a carbon dioxide profile, impurity profile, osmolality profile, viable cell concentration profile, and pH profile.

16. A prediction system for a batch-type manufacturing process associated with a finite duration, the prediction system comprising:

one or more sensors for measuring values of a plurality of variables of the manufacturing process including at least one dependent variable that represents a process parameter whose value is dependent on one more process conditions, wherein the measure values include measured values of the plurality of variables associated with at least one historical batch run and measured values of the plurality of variables associated with at least one current batch run; and

a prediction module for estimating an unknown future value of the at least one dependent variable at a future point in time in the at least one current batch run using a partial least squares (PLS) regression approach, the prediction module including;

a calibration component that is configured to generate (1) a X matrix including the measured values of the plurality of variables associated with the at least one historical batch run;

(2) a Y matrix including the measured value of the at least one dependent variable associated with the at least one historical batch run; and

(3) a relationship between the X matrix and the Y matrix determined based on the PLS regression approach, wherein the relationship is represented by a β

matrix; and

an estimation component that is configured to estimate the unknown future value of the at least one dependent variable in the at least one current batch run using the β

matrix and the measured values of the plurality of variables associated with the at least one current batch run.

17. The prediction system of claim 16, wherein the plurality of variables of the manufacturing process further include at least one manipulated variable that represents a process parameter whose value is assignable during the manufacturing process.

18. The prediction system of claim 17, further comprising an input module for receiving at least one future value of the at least one manipulated variable associated with the at least one current batch run, wherein the estimation component of the prediction module is configured to estimate the unknown future value of the at least one dependent variable using (1) the relationship represented by the β

matrix, (2) the measured values of the plurality of variables associated with the at least one current batch run, and (3) the at least one future value of the at least one manipulated variable associated with the at least one current batch run.

19. The prediction system of claim 16, further comprising an analysis module for performing multivariate analysis on a combination of (1) the measured values of the plurality of variables and (2) the estimated future value of the at least one dependent variable to generate a plurality of multivariate statistics for the at least one current batch run.

20. The prediction system of claim 16, wherein the prediction module is configured to run the PLS regression approach once without iteration to determine the β

matrix.