The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 26, 2013

Filed:

Sep. 30, 2008
Applicants:

Brian Kent Stephenson, Georgetown, TX (US);

David G. Hoch, Falmouth, ME (US);

L. Paul Collete, Iii, Westminster, MA (US);

Inventors:

Brian Kent Stephenson, Georgetown, TX (US);

David G. Hoch, Falmouth, ME (US);

L. Paul Collete, III, Westminster, MA (US);

Assignee:

Rockwell Automation Technologies, Inc., Mayfield Heights, OH (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 7/66 (2006.01); G05B 13/00 (2006.01); D21G 9/00 (2006.01); G05B 17/02 (2006.01); G05B 13/04 (2006.01);
U.S. Cl.
CPC ...
D21G 9/0009 (2013.01); D21G 9/0027 (2013.01); G05B 17/02 (2013.01); G05B 13/042 (2013.01);
Abstract

A technique is disclosed for optimizing a quality parameter in a process that is not directly measurable online using conventional measurement devices. The technique includes the use of a first inferential model to predict a value for the parameter based upon other process variables. A second inferential model predicts a residual component of the process parameter based off non-controllable residual variables of the process. The inferential model outputs are combined to produce a composite predicted value which may be further adjusted by an actual prediction error determined via comparison with an offline measurement. The adjusted predicted value is provided to a dynamic predictive model which may be adapted to implement control actions to drive or maintain the quality parameter at a target set point. The technique may further consider cost optimization factors and production reliability factors in order to produce a product meeting the target quality set point or range while considering production requirements and minimizing overall costs.


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