Abstract: A method for building robust model predictive controller universally applicable is presented based on the innate process characteristics independent of the method of control actuation. The method of universal MPC design permits proper configuration of requisite regulatory control loops for measured and unmeasured disturbance rejections consistent with the underlying innate process characteristics and their embedding within the overall process unit model predictive controller. The method of universal MPC design requires that manipulated variables process value based model (PV-based models) be used in control and optimization in place of the customary set point based models (SP-based models) or control output based models (OP-based models). The PV-based models are devoid of the manipulated variables regulatory controllers response and tuning. Based on the PV-based models, an alternate method of MPC called PV-based MPC is presented that is most robust and adaptable of possible three types of MPC.

Abstract: A system and method of modeling, prediction, optimization and controlling an embedded alarm control having a plurality of independently controlled, manipulated variables, at least one controlled variables and one or more disturbance variables. The method includes determining simultaneously a set number of dynamic moves of the manipulated variables along with steady state values of the manipulated and controlled variables with steady state constraints relating to the manipulated and controlled variables as well as dynamic constraints relating to the manipulated and controlled variables including relating to the disturbance variables. Embedding alarm controls in the simultaneous dynamic control and steady state optimization with varying type of alarming situations and aiding operator in recovery actions.

Abstract: The present invention relates to a steady state optimization method incorporating dynamic variance correction for dynamic variations of both independent variables and dependent variables of a dynamic system. The dynamic variance correction is based on measured variance of the variables and a weighing factor for each of the variables. The dynamic variance correction offers an effective method of dynamic violations avoidance of controlled variables for a model predictive controller without having to constantly adjust the tuning weights in response to changing dynamical conditions.

Abstract: A method of dynamic model predictive control is presented in which both steady state optimization and dynamic moves calculation of the manipulated variables are determined together as part of one optimization solution (211). The method combines steady state optimization and dynamic move calculation such that the steady state optimal targets are determined consistent with the dynamic moves and the resulting dynamic response of the process so that the controlled variables do not violate their low/high limits in both steady state and dynamically. The method utilizes what is described as high limit dynamic violation variables and low limit dynamic violation variables corresponding to each of the controlled variables.

Abstract: A system and method for performing modeling, prediction, optimization and control, which would permit what is, described herein as embedded alarm control. In the proposed method of embedding alarm control, the alarm limits are used to constrain the controlled variables dynamically such that the controller actions are affected to minimize and prevent alarm limit violations in addition to the constraints commonly included. It is proposed in the present invention that the alarm limits be incorporated as part of optimization and constrained control action calculation in a model predictive control. As an exemplary case, this method of incorporation of alarm limits within optimization and control is described and the results obtained from it are presented.

Abstract: A method for building robust model predictive controller universally applicable is presented based on the innate process characteristics independent of the method of control actuation. The method of universal MPC design permits proper configuration of requisite regulatory control loops for measured and unmeasured disturbance rejections consistent with the underlying innate process characteristics and their embedding within the overall process unit model predictive controller. The method of universal MPC design requires that manipulated variables process value based model (PV-based models) be used in control and optimization in place of the customary set point based models (SP-based models) or control output based models (OP-based models). The PV-based models are devoid of the manipulated variables regulatory controllers response and tuning. Based on the PV-based models, an alternate method of MPC called PV-based MPC is presented that is most robust and adaptable of possible three types of MPC.

Abstract: A method of dynamic model predictive control is presented in which both steady state optimization and dynamic moves calculation of the manipulated variables are determined together as part of one optimization solution (211). The method combines steady state optimization and dynamic move calculation such that the steady state optimal targets are determined consistent with the dynamic moves and the resulting dynamic response of the process so that the controlled variables do not violate their low/high limits in both steady state and dynamically. The method utilizes what is described as high limit dynamic violation variables and low limit dynamic violation variables corresponding to each of the controlled variables.

Abstract: The present invention relates to a steady state optimization method incorporating dynamic variance correction for dynamic variations of both independent variables and dependent variables of a dynamic system. The dynamic variance correction is based on measured variance of the variables and a weighing factor for each of the variables. The dynamic variance correction offers an effective method of dynamic violations avoidance of controlled variables for a model predictive controller without having to constantly adjust the tuning weights in response to changing dynamical conditions.

Abstract: A modular formulation of model predictive controller is proposed by this invention (400). The modular formulation offers a robust model predictive controller with unified method of tuning. Explicit use of material and energy balance provides stable and robust performance (412). Use of economic based tuning offers a unified tuning method (411). The unified tuning method establishes consistency of control actions resulting from the steady state optimization and the dynamic control (400). Further, a method for formulating and tuning integrated optimization and control using multiple modular model predictive controllers is presented in this invention (100). The method incorporates a direct and explicit method of integration of a number of modular model predictive controllers. The integration of the modular controllers is achieved seamlessly without requiring any further tuning adjustments (200).

Abstract: A method is presented for updating process models (100) used in a model predictive controller when a change has been made in any regulatory controller configuration and/or it's tuning without having to conduct new full identification testing of the process. The method employs Core Process Models of Controlled Variables (101) and Manipulated Variables (103), devoid of dynamics of interacting regulatory controllers in conjunction with Manipulated Variables Disturbance Rejection Models (104). The process models (100) can be updated for use for on-line control as well as for off-line simulation studies including operator training. This allows updating of a complex multivariable process control with relatively ease with absolute minimal of additional data gathering.