Computer-aided modeling and manufacture of products

• US 7,233,834 B2
• Filed: 07/02/2004
• Issued: 06/19/2007
• Est. Priority Date: 08/13/2003
• Status: Expired due to Fees

• Current Assignee: Cargill, Inc. (Agricultural Services Division)
• Original Assignee: Cargill, Inc. (Agricultural Services Division)
• Inventors: Anderson, Kevin R., Smith, Sean A., McDonald, John T. Jr., Weeks, Norman J. III
• Primary Examiner(s): CABRERA, ZOILA E
• Application Number:
  US10/884,622
• Publication Number:
  US 20050038538A1
• Time in Patent Office: 1,082 Days
• Field of Search: 700/108, 700/90, 700/1, 700/28, 702/183, 702/184
• US Class Current: 700/108
• CPC Class Codes: G05B 13/0255 the criterion being a time-optimal performance criterion
• CPC Class Codes: G05B 17/02 electric

Abstract

Disclosed are modeling and process control techniques for manufacturing products. More specifically, computer-aided modeling techniques are described that allow the manufacturer to predict a profile for a multivariate output that is necessary to achieve a target performance property for a manufactured product.

Claims

1. A method comprising:

developing a reverse chemometric model by;

(a) developing a first regression model to calibrate a multivariate output with a preselected performance property of a product;

(b) developing a second regression model to calibrate the multivariate output with at least one operating parameter that affects a value of the preselected performance property; and

(c) developing a third regression model to predict a version of the multivariate output from the preselected performance property;

receiving a communication from a business unit via a computer network, wherein the communication specifies a value for the performance property of the product;

invoking the reverse chemometric model to calculate a multivariate output and an operating parameter based on the specified value to achieve the performance property; and

communicating the operating parameter to a remote facility for manufacturing the product according to the operating parameter.

2. The method of claim 1, wherein developing a third regression model comprises developing a third regression model to predict a compressed, statistically constrained version of the multivariate output from the preselected performance property.

3. The method of claim 1, wherein invoking the reverse chemometric model comprises:

convolving the predicted multivariate output with a predetermined template of the multivariate output to create a de-compressed vector; and

using the de-compressed vector of the predicted multivariate output to predict the at least one operating parameter to yield the preselected performance property.

4. The method of claim 1, wherein invoking a reverse chemometric model comprises:

calculating the predicted multivariate output using the third regression model;

applying the second regression model to the predicted multivariate output to calculate the operating parameter; and

using the operating parameter to produce the desired value of the preselected performance property.

5. The method of claim 4, wherein selecting the desired value of the preselected performance property comprises selecting the desired value based on data selected from a group consisting of pricing for the product, sales volumes of the product, a geographic location of a facility for manufacturing the product, customer preference information for the product, and a current level of inventory of the product.

6. The method of claim 4, wherein calculating the predicted multivariate output comprises developing a reverse model in which multivariate output data serves as a predicted variable, and the preselected performance property for the product serves as a prediction variable.

7. The method of claim 4, wherein calculating the predicted multivariate output comprises developing a computation model from which the multivariate output can be predicted based on the performance properties of the manufactured product.

8. The method of claim 4, wherein calculating the predicted

multivariate output comprises:

producing the product;

analyzing the product to generate multivariate output data for the product;

conducting performance tests on the product to generate performance property data for the preselected performance property; and

applying chemometric techniques to develop a correlation between the multivariate output and the performance property data.

9. The method of claim 8, wherein applying chemometric techniques comprises:

representing the multivariate output data as a polynomial; and

regressing the multivariate output polynomial against the performance property data.

10. The method of claim 9, wherein regressing coefficients of the polynomial comprises using a Partial Least Squares regression algorithm.

11. The method of claim 9, wherein applying chemometric techniques comprises:

estimating a range for an order for the multivariate output polynomial;

estimating a range for an order for a regression polynomial;

iterating over the estimated ranges to generate combinations of multivariate output

polynomials and regression polynomials;

calculating residuals for the combinations; and

selecting one of the combinations based on the residuals.

12. The method of claim 1, wherein the predicted multivariate output is selected from the group consisting of particle size distributions, rheology profiles, texture profiles, gas chromatography outputs, liquid chromatography outputs, thermal analysis outputs, infra red spectroscopy outputs, Raman spectroscopy outputs, and optical absorption spectroscopy outputs.

13. The method of claim 1, wherein the at least one operating parameter is selected from the group consisting of a reaction time, a reaction temperature, an addition rate for the reactants, a concentration of the reactants, and mixing intensity of a reaction.

14. The method of claim 1, wherein the product is selected from a group consisting of monomers, ionomers, oligomers, polymers, inorganic particles, and network polymers including polysaccharides.

15. The method of claim 1, wherein the product comprises a paper product, and the preselected performance property of the paper product is selected from the group consisting of surface strength, internal bond, burst strength, tensile strength, tear strength, porosity, short span compression (SCT), dynamic contact angle, and starch retention.

16. A method comprising:

determining a set of price levels for a product, wherein the price levels correspond to values of a performance property of the product;

calculating multivariate output profiles and operating parameters to achieve the values of the performance property;

constructing a reverse chemometric model from the multivariate output profiles;

receiving a communication from a business unit via a computer network, wherein the communication specifies a value for the performance property of the product;

invoking the reverse chemometric model to calculate a multivariate output based on the specified value and an operating parameter to achieve the specified value for the performance property; and

communicating the operating parameter to a remote facility for manufacturing the product according to the operating parameter.

17. The method of claim 16, wherein the reverse chemometric model calculates a multivariate output by calculating a particle size distribution (PSD) for the product.

18. The method of claim 16, further including selling the product according to the respective price levels.

19. A system comprising:

one or more computers interconnected by a network;

a first software module executing on at least one of the computers to calculate multivariate output profiles and operating parameters to achieve values of a performance property of a product, wherein the values for the performance property correspond to price levels for the product;

a second software module executing on at least one of the computers to construct a reverse chemometric model from the multivariate output profiles;

a third software module executing on at least one of the computers to present an interface to receive a selection of the performance property of the product, wherein the third software module applies the reverse chemometric model to predict a multivariate output from the selected performance property of the product; and

a fourth software module executing on at least one of the computers to communicate the operating parameter to a remote facility for manufacturing the product according to the operating parameter.

20. The system of claim 19, wherein the third software module comprises a web server and the interface comprises a user interface to receive the selection of the performance property of the product.

21. The system of claim 20, further comprising a client computer located within a manufacturing facility and coupled to the web server via a network, wherein the web server selects an operating parameter based on the predicted multivariate output and communicates the operating parameter to the client computer for manufacturing the product according to the operating parameter.

22. The system of claim 21, wherein the operating parameter is selected from the group consisting of a reaction time, a reaction temperature, an addition rate for the reactants, a concentration of the reactants, and mixing intensity for the reaction.

23. The system of claim 19, wherein the third software module comprises an application programming interface (API) to programmatically receive the selection of the performance property.

24. The system of claim 19, wherein the product comprises a paper product, and the preselected property of the paper product is selected from the group consisting of surface strength, internal bond, burst strength, tensile strength, tear strength, porosity, sbort span compression (SCT), dynamic contact angle, and starch retention.

25. The system of claim 19, wherein the interface provides input areas for a user to provide one selected from a group consisting of pricing for the product sales volumes of the product, a geographic location of a facility for manufacturing the product, customer preference information for the product, and a current level of inventory of the product.

26. A computer-readable medium comprising instructions therein to cause a computing device to:

calculate multivariate output profiles and operating parameters to achieve values of a performance property of a product, wherein values of the performance property of the product correspond to price levels for the product;

construct a reverse chemometric model from the multivariate output profiles;

select a value for the performance property of the product;

apply the reverse chemometric model to calculate a multivariate output based on the selected value;

select an operating parameter based on the calculated multivariate output; and

communicate the operating parameter to a facility for manufacturing the product according to the selected operating parameter.

27. The computer-readable medium of claim 26, further comprising instruction causing the computing device to develop the reverse chemometric model as a model in which multivariate output data serves as a predicted variable, and the performance property for the product serves as a prediction variable.

28. The computer-readable medium of claim 26, further comprising instruction causing the computing device to develop a computation model from which the multivariate output can be predicted based on the performance properties of the product.

29. The computer-readable medium of claim 26, further comprising instruction causing the computing device to apply chemometric techniques to develop a correlation between the multivariate output data and the performance property data.

30. The computer-readable medium of claim 26, further comprising instruction causing the computing device to:

receive input defining a range for an order for a the multivariate output polynomial;

receive input defining a range for an order for a regression polynomial;

iterate over the ranges to generate combinations of multivariate output polynomials and regression polynomials;

calculate residuals for the combinations; and

select one of the combinations based on the residuals.

31. A method comprising:

developing the reverse chemometric model by;

a. developing a first regression model to calibrate a multivariate output with a performance property of a product;

b. developing a second regression model to calibrate the multivariate output with at least one operating parameter that affects the value of the performance property; and

c. developing a third regression model to predict a compressed, statistically constrained version of the multivariate output from the performance property and convolving the predicted multivariate output with a predetermined template of the multivariate output to create a de-compressed vector of the predicted multivariate output that is used to predict the at least one operating parameter to yield the performance property wherein invoking a reverse chemometric model comprises;

d. calculating the predicted multivariate output using the third regression model and the template; and

e. applying the second regression model of step (b) to the predicted multivariate output of step (d), to calculate the at least one operating parameter;

receiving a communication from a business unit via a computer network, wherein the communication specifies a value for the performance property of the product;

invoking the reverse chemometric model to calculate a multivariate output based on the specified value and an operating parameter to achieve the performance property; and

communicating the operating parameter to a remote facility for manufacturing the product according to the operating parameter.