Honeywell G.m.b.h.

Other

Honeywell International Inc.

Zhuxin J Lu

Brian C Horn

J Ward MacArthur

J Ward Mac Arthur

A control system and a method of operating the same are introduced that globally optimize associated processes within a process facility. The control system includes a global controller and many local controllers. The global controller monitors characteristics of the associated processes and generates, in response thereto, control data for optimizing the process facility. The local controllers monitor the associated processes and operate in accordance with the control data to varying degrees to control the monitored associated processes, thereby cooperating with the global controller to optimize the process facility.

System and methods for globally optimizing a process facility

A control system and a method of operating the same are introduced that globally optimize associated processes within a process facility. The control system includes a global controller, a bridge predictor and many local controllers. The global controller monitors characteristics of the associated processes and generates, in response thereto, control data for optimizing the process facility. The bridge predictor modifies at least a portion of the control data in response to relationships among the associated processes. The local controllers monitor the associated processes and operate in accordance with the control data to varying degrees to control the monitored associated processes, thereby cooperating with the global controller to optimize the process facility.

Systems and methods using bridge models to globally optimize a process

In a process control system, having a controller for providing robust control of a process, the controller utilizes a predetermined range control procedure the results being a control solution. The process has at least one manipulated variable and at least one controlled variable. A method of providing dynamic optimization comprises the steps of calculating an optimized set of steady state values of the manipulated variables and the controlled variables. An augmented range control procedure is created to harmonize the set of steady state values with the control solution. The augmented range control procedure is solved, the manipulated variables generated thereby being compatible with the set of steady state values and with the control solution. The process is controlled in accordance with the manipulated variables generated by the augmented range control procedure, thereby providing optimal control of the process.

Method of process controller optimization in a multivariable predictive

A process control system which includes at least one manipulated variable and at least one controlled variable, provides a method for robust control of a process. Predetermined constraints of the manipulated variables and the controlled variables, and the present values of the manipulated variables are obtained. A set of scale factors for the manipulated variables and the process variables are calculated. The controller is initialized with the set of scale factors, the scale factors determining the relative importance of the manipulated variables and the process variables to the process. New values are calculated for the controlled variables for a predetermined number of points in the future, such that the values of the controlled variables are within the predetermined range thereby obtaining an optimal robustness of the resultant controller. The manipulated variables are also calculated to be within predetermined constraints, and the controlled variables to fall within a predetermined range when controllable. From a plurality of solutions, a most robust solution is selected. Then the manipulated variables are adjusted to cause the process control system to drive the values of the controlled variables to the calculated values.

Method of optimal scaling of variables in a multivariable predictive

A process control system which includes at least one manipulated variable and at least one controlled variable, provides a method for robust control of a process. Predetermined constraints of the manipulated variables and the controlled variables, and the present values of the manipulated variables are obtained. The controller is loaded with parameters which define an optimal controller, the parameters being calculated off-line. To determine the parameters a single min-max statement is defined for a worst case model of the process which operates in conjunction with a best case controller. The single min-max statement is converted to a corresponding canonical expression in the form of a minimization problem, the resultant solution of the minimization problem being the parameter. New values are calculated for the controlled variables for a predetermined number of points in the future, such that the values of the controlled variables are within the predetermined range thereby obtaining an optimal robustness of the resultant controller. The manipulated variables are also calculated to be within predetermined constraints, and the controlled variables to fall within a predetermined range when controllable. From a plurality of solutions, a most robust solution is selected. Then the manipulated variables are adjusted to cause the process control system to drive the values of the controlled variables to the calculated values.

Method of optimal controller design for multivariable predictive control

A process control system which includes at least one manipulated variable and at least one controlled variable, provides a method for robust control of a process. Predetermined constraints of the manipulated variables and the controlled variables, and the present values of the manipulated variables are obtained. New values are calculated for the controlled variables for a predetermined number of points in the future, such that the values of the controlled variables are within the predetermined range thereby obtaining an optimal robustness of the resultant controller. The manipulated variables are also calculated to be within predetermined constraints, and the controlled variables to fall within a predetermined range when controllable. From a plurality of solutions, a most robust solution is selected. Then the manipulated variables are adjusted to cause the process control system to drive the values of the controlled variables to the calculated values.

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Glendale, AZ, United States of America

Zhuxin J Lu