Alexander J Federowicz

US Patent:

56404913, Jun 17, 1997

Filed:

Dec 18, 1995

Appl. No.:

8/574250

Inventors:

Naveen V. Bhat - Houston TX
William B. Braden - Houston TX
Timothy J. Graettinger - Bethel Park PA
Alexander J. Federowicz - Pittsburgh PA
Paul A. Dubose - Chapel Hill NC

Assignee:

Texaco, Inc.
Neuralware, Inc.

International Classification:

G05B 1302

US Classification:

395 22

Abstract:

A control system having four major components: a target optimizer, a path optimizer, a neural network adaptation controller and a neural network. In the target optimizer, the controlled variables are optimized to provide the most economically desirable outputs, subject to operating constraints. Various manipulated variable and disturbance values are provided for modeling purposes. The neural network receives as inputs a plurality of settings for each manipulated and disturbance variable. For target optimization all the neural network input values are set equal to produce a steady state controlled variable value. The entire process is repeated with differing manipulated variable values until an optimal solution develops. The resulting target controlled and manipulated variable values are provided to the path optimizer to allow the manipulated variables to be adjusted to obtain the target output. Various manipulated variable values are developed to model moves from current to desired values.

US Patent:

54774446, Dec 19, 1995

Filed:

Sep 14, 1992

Appl. No.:

7/944645

Inventors:

Naveen V. Bhat - Houston TX
William B. Braden - Houston TX
Timothy J. Graettinger - Bethel Park PA
Alexander J. Federowicz - Pittsburgh PA
Paul A. DuBose - Chapel Hill NC

International Classification:

G06F 1546

US Classification:

364152

Abstract:

A control system having four major components: a target optimizer, a path optimizer, a neural network adaptation controller and a neural network. In the target optimizer, the controlled variables are optimized to provide the most economically desirable outputs, subject to operating constraints. Various manipulated variable and disturbance values are provided for modeling purposes. The neural network receives as inputs a plurality of settings for each manipulated and disturbance variable. For target optimization all the neural network input values are set equal to produce a steady state controlled variable value. The entire process is repeated with differing manipulated variable values until an optimal solution develops. The resulting target controlled and manipulated variable values are provided to the path optimizer to allow the manipulated variables to be adjusted to obtain the target output. Various manipulated variable values are developed to model moves from current to desired values.