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A first principles, steady state model of a desired polymer process is applied with a non-linear optimizer to a linear controller. Model process gains and optimal target values for controller variables result. These results are utilized by a multivariable linear controller to achieve nonlinear contr

A first principles, steady state model of a desired polymer process is applied with a non-linear optimizer to a linear controller. Model process gains and optimal target values for controller variables result. These results are utilized by a multivariable linear controller to achieve nonlinear control of the subject process. Preferably the nonlinear optimizer is DMO/SQP. The steady state model is produced by Polymers Plus and the linear controller is DMCplus, all of Aspen Technology, Inc. in Cambridge Mass.

대표청구항 ▼

1. Computer apparatus for controlling a non-linear manufacturing process comprising: a controller for determining and adjusting manipulated variable values for controlling a subject non-linear manufacturing process, the controller employing a linear model for modeling effect that would result in

1. Computer apparatus for controlling a non-linear manufacturing process comprising: a controller for determining and adjusting manipulated variable values for controlling a subject non-linear manufacturing process, the controller employing a linear model for modeling effect that would result in the subject manufacturing process with a step change in manipulated variable values; and an optimizer coupled to the controller for updating the linear model of the controller, as the subject non-linear manufacturing process transitions from one operating point to another, in a high degree of non-linearity between manipulated variables and controlled variables of the subject process, the controlled variables being indicative of physical properties of the subject process, the optimizer utilizing a non-linear model of the subject process for updating the linear model and determining target values of the controlled variables. 2. Computer apparatus as claimed in claim 1 wherein the controller employs dynamic linear models. 3. Computer apparatus as claimed in claim 1 further comprising a source of sensor measured variables for representing the controlled variables of the subject process. 4. Computer apparatus as claimed in claim 3 wherein the non-linear model of the optimizer determines gains between the manipulated variables and the sensor measured controlled variables, and the optimizer gain adapts the linear model of the controller with the determined gains. 5. Computer apparatus as claimed in claim 1 wherein the optimizer is executed as frequently as the controller. 6. Computer apparatus as claimed in claim 1 wherein the non-linear model of the optimizer is a rigorous, first principles, non-linear model. 7. Computer apparatus as claimed in claim 1 wherein the subject non-linear manufacturing process is a polymer process. 8. A method for controlling a non-linear manufacturing process comprising the computer implemented steps of: utilizing a linear model, modeling effect that would result in a subject manufacturing process with a step change in manipulated variable values used for controlling said process; using a non-linear model of the subject process, (i) updating the linear model as the subject process transitions from one operating point to another, in a high degree of non-linearity between the manipulated variables and controlled variables indicative of physical properties of the subject process, and (ii) determining target values of the controlled variables. 9. A method as claimed in claim 8 wherein the step of utilizing a linear model includes dynamic linear models. 10. A method as claimed in claim 8 further comprising the step of providing sensor measured variables for representing various controlled variables of the subject process. 11. A method as claimed in claim 10 further comprising the step of determining process gains between the manipulated variables and the sensor-measured controlled variables using the non-linear model; and the step of updating the linear model includes gain adapting the linear model with the determined gains. 12. A method as claimed in claim 8 wherein the step of updating is executed as frequently as the subject process transitions from one operating point to another. 13. A method as claimed in claim 8 wherein the step of using a non-linear model includes using a rigorous, first principles, non-linear model. 14. A method as claimed in claim 8 wherein the subject process is a polymer process.