Process 1 I t 1 Human

US 20060218107A1

(19) United States (12) Patent Application Publication (10) Pub. No.: US 2006/0218107 A1 Young (43) Pub. Date: Sep. 28, 2006 (54)

METHOD FOR CONTROLLING A PRODUCT

Publication Classi?cation

PRODUCTION PROCESS

(51)

(75) Inventor: Timothy M. Young, Knoxville, TN

(Us) Correspondence Address:

(52)

LUEDEKA, NEELY & GRAHAM, P.C.

Int. Cl. G06N 3/00 G06N 3/12 G06F 15/18 C01F 15/00 C01G 43/00 US. Cl.

.............................................................. .. 706/13

(57)

P 0 BOX 1871

(2006.01) (2006.01) (2006.01) (2006.01) (2006.01)

ABSTRACT

A method for controlling a production process involving selection of process variables a?‘ecting product characteris tics and using genetic algorithms to modify a set of seed

KNOXVILLE, TN 37901 (US)

(73) Assignee: The University of Tennessee Research Foundation

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

(21) Appl, No.1

11/088,651

projected product characteristic. Aproduction control opera tor uses the list of process variables and knowledge of

(22)

Filed:

10

associated process control settings to control the production process.

Mar. 24, 2005

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Patent Application Publication Sep. 28, 2006 Sheet 1 of 14 1O

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US 2006/0218107 A1

Patent Application Publication Sep. 28, 2006 Sheet 2 of 14 50

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US 2006/0218107 A1

Patent Application Publication Sep. 28, 2006 Sheet 3 of 14

US 2006/0218107 A1

100

102\

Select a source for the data ?le to be used to

generate the neural network model.

Select the end products that are to be used for

generating the neural network model.

Choose the statistical method for selecting parameters that will be used for generating the neural network model.

Identify process parameters that will be excluded ?'om the neural network model.

Choose the number of parameters that will be included in the neural network model. 7

Choose the start date and end date for data to be used in the neural network model.

Select the parameters that will be included in the neural network model (automatic, based upon

statistical method for selecting parameters).

Adjust options. (Optional) 7

Run the genetic algorithm software to generate the neural network model.

Figure 3

Patent Application Publication Sep. 28, 2006 Sheet 4 0f 14

Fig. 4

US 2006/0218107 A1

Patent Application Publication Sep. 28, 2006 Sheet 5 0f 14

I P‘rbduct Width

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.75 Thick, 51 Wide, 48 Density Delete Product

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US 2006/0218107 A1

Patent Application Publication Sep. 28, 2006 Sheet 6 0f 14

Fig. 6

US 2006/0218107 A1

Patent Application Publication Sep. 28, 2006 Sheet 7 0f 14

US 2006/0218107 A1

é Select the Parameters to be Excluded ' ' ‘

Excluded while Modelling

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Patent Application Publication Sep. 28, 2006 Sheet 8 0f 14

US 2006/0218107 A1

Step 5 of 7 - Number of Parameters

Choose the ‘number of parameters to be used for the GANN Model.~ Clicking next w?l start JMR arid the parameter selection process will commence.

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Choose the start and ertct dates for the portion of the data-to beqsed in the GANN mociel}; '

Start Date

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End Date _

P‘lnclude all dates greater than the stat date;

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Patent Application Publication Sep. 28, 2006 Sheet 9 0f 14

US 2006/0218107 A1

' Initial GA Parameters - {:23}

Hines: Fraction Hank ;"———~" """"""""""""""""" m“,

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Step 7 of 7 - Confirm seleoteo-Parameters. and Adiust Advanced GANN options

Review the 20 parametersio be (reed for the GANN Modet

1. IMAL_B1_Ave_T 0t 2. JIEI304W

3. Mat_D ensity 4. Mat_Dry_Weighl 5. MALT EMF 8. MT4U1 9 7. PIC_4310PV .

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8. PH_Acl_Pos_Press_Frame__23_Hight 9. PFi_Ac!_Pres_Frame_1l]_Lell 10. PR_Acl_Pres_Frame_27_Right 11 , PFLD i:t_5 P_Frame_l]7

12. PFLD ist_SP_Frame_12

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Patent Application Publication Sep. 28, 2006 Sheet 10 0f 14 i. GANN (Running)

lnter'nél'Bohd‘és Predict'ed by GANN

‘130 "125 ‘

US 2006/0218107 A1

Patent Application Publication Sep. 28, 2006 Sheet 11 of 14

US 2006/0218107 A1

130

\ Store (a) raw and intermediate material properties, 132 \ (b) process control variables, and (0) end product property test values in a data Warehouse.

134 \ Identify the raw and intermediate material properties and process control variables that are most

in?uential in determining an end product property.

136

Quasi-randomly generate seed neural networks comprising heuristic equations predicting the end \ product property based upon raw and intermediate material properties and process control variables.

138 x 140

142 \ Use a genetic algorithm processor to modify the seed neural networks and arrive at the best GA

model for predicting the end product property.

144

Input real-time raw and intermediate material

\ properties and real~time process control data and predict the end product property. 7

Provide a human operator with the predicted end 146 \ product property and the prioritized list of raw material properties and process parameters that

in?uence the end product property.

7

Analyze residual errors in the best GA model to

148 '\ select material samples for laboratory testing to generate additional end product property test values.

Figure 12

/ 150

Patent Application Publication Sep. 28, 2006 Sheet 12 of 14

US 2006/0218107 A1

XY scatter plot of actual and predicted internal bond for product type 3.

Figure 13

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XY scatter plot of actual and predicted internal bond for product type 8.

Figure 14

Patent Application Publication Sep. 28, 2006 Sheet 13 of 14

US 2006/0218107 A1

XY scatter plot of actual and predicted internal bond for product type 9.

Figure 15

XY scatter plot of actual and predicted internal bond for product type 7.

Figure 16

Patent Application Publication Sep. 28, 2006 Sheet 14 of 14

a

US 2006/0218107 A1

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XY scatter plot of actual and predicted internal bond for product type 4.

Figure 17 220

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