Control system using an adaptive neural network for target and path optimization for a multivariable, nonlinear process

US5640491A · kind A · utility

Assignees

• Texaco Inc. · US
• Neuralware, Inc. · US

Inventors

• Naveen V. Bhat · Houston, US
• William B. Braden, Jr. · Houston, US
• Kent E. Heckendoorn · Houston, US
• Timothy J. Graettinger · Bethel Park, US
• Alexander J. Federowicz · Pittsburgh, US
• Paul DuBose · Chapel Hill, US

Key dates

• Filing date: Dec 18, 1995
• Grant date: Jun 17, 1997
• Expiry date: Dec 18, 2015

Classification

• Technology area (CPC B): Performing Operations; Transporting
• CPC primary: B01J19/0033
• WIPO field: Chemical engineering
• WIPO sector: Chemistry

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. In this case trend indicating values of the manipulated and disturbance variables are provided to produce time varying values of the controlled variables. The process is repeated until an optimal…