Method for controlling a product production process

• US 20060218107A1
• Filed: 03/24/2005
• Published: 09/28/2006
• Est. Priority Date: 03/24/2005
• Status: Abandoned Application

• Current Assignee: University of Tennessee Research Foundation (University of Tennessee)
• Original Assignee: University of Tennessee Research Foundation (University of Tennessee)
• Inventors: Young, Timothy M.
• Application Number:
  US11/088,651
• Publication Number:
  US 20060218107A1
• Time in Patent Office: Days
• US Class Current: 706/13
• CPC Class Codes: G05B 13/027 using neural networks only
• CPC Class Codes: G06N 3/045 Combinations of networks

Abstract

A method for controlling a production process involving selection of process variables affecting product characteristics and using genetic algorithms to modify a set of seed neural networks based upon the process variables to an create an optimal neural network model. A commercial statistical software package may be used to select the process variables. Real-time process control data are fed into the optimal neural network model and used to calculate a projected product characteristic. A production control operator uses the list of process variables and knowledge of associated process control settings to control the production process.

Claims

1. A method for controlling a process for producing a product, the method comprising:

providing a set of seed neural networks corresponding to the process;

using genetic algorithm software to genetically operate on the seed neural networks to predict a characteristic of the product made by the process;

based upon the predicted characteristic of the product, manually adjusting the process to improve the predicted characteristic of the product.

11. The method of claim 10 wherein the step of calculating a projected product characteristic comprises calculating residual errors and the method further comprises the step of analyzing the residual errors and selecting at least one material sample for laboratory testing to acquire additional product characteristic data.

2. A method for controlling a process for producing a product, the method comprising:

providing process variable data associated with a product characteristic data, a set of process variables that are influential in affecting a product characteristic, and seed neural networks incorporating the process variables and the product characteristic;

using genetic algorithm software to genetically operate on the seed neural networks and arrive at an optimal model for predicting the product characteristic based upon the process variable data associated with the product characteristic data;

inputting process control data from the product production process into the optimal model and using the process control data to calculate a projected product characteristic;

based on the projected product characteristic, manually adjusting at least one process variable to control the process.

3. The method of claim 2 wherein the projected product characteristic comprises a product output rate.

4. The method of claim 3 wherein the projected product characteristic comprises a material consumption rate.

5. The method of claim 4 further comprising the step of updating process variable data in real time.

6. The method of claim 5 wherein the step of calculating a projected product characteristic comprises calculating residual errors and the method further comprises the step of analyzing the residual errors and selecting at least one material sample for laboratory testing to generate additional product characteristic data.

7. The method of claim 2 wherein the projected product characteristic comprises a material consumption rate.

8. The method of claim 7 further comprising the step of updating process variable data in real time.

9. The method of claim 8 wherein the step of calculating a projected product characteristic comprises calculating residual errors and the method further comprises the step of analyzing the residual errors and selecting at least one material sample for laboratory testing to acquire additional product characteristic data.

10. The method of claim 2 further comprising the step of updating process variable data in real time.

12. The method of claim 2 wherein the step of calculating a projected product characteristic comprises calculating residual errors and the method further comprises the step of analyzing the residual errors and selecting at least one material sample for laboratory testing to acquire additional product characteristic data.

16. The process of claim 12 wherein step (a) comprises providing a parametric dataset that includes median values of material properties.

13. A method for generating a neural network model for a product production process, the method comprising:

(a) providing a parametric dataset that associates process variable data with product characteristic data;

(b) generating a set of seed neural networks using the parametric dataset;

(c) defining a fitness fraction ranking order, genetic algorithm proportion settings, and a number of passes per data partition for a genetic algorithm software code;

(d) using the genetic algorithm software code to modify the seed neural networks and create an optimal model for predicting a product characteristic based upon the process variable data.

14. The process of claim 13 further comprising selecting process variable data that will be excluded from the genetic algorithm model.

15. The process of claim 14 wherein step (a) comprises providing a parametric dataset that includes median values of material properties.

17. A method for controlling a product production process, the method comprising:

providing a parametric dataset that associates process variable data with product characteristic data;

quasi-randomly generating a set of seed neural networks using the parametric dataset;

using a genetic algorithm software code to create an optimal model from the set of seed neural networks;

inputting process control data from the product production process into the optimal model and using the process control data to calculate a projected product characteristic;

based on the projected product characteristic, adjusting at least one process variable to control the process.

18. The method of claim 17 wherein the projected product characteristic comprises a product output rate.

19. The method of claim 17 wherein projected product characteristic comprises a material consumption rate.

20. The method of claim 17 further comprising the step of updating process variable data in real time.

21. The method of claim 17 wherein the step of calculating a projected product characteristic comprises calculating residual errors and the method further comprises the step of analyzing the residual errors and selecting at least one material sample for laboratory testing to acquire additional product characteristic data.