Abstract: Model predictive control is used to obtain the best performance value for an objective in a dynamic environment. One or more best performance values for a model predictive application are obtained by utilizing asymmetric dynamic behavior that pushes the process to the edges of the operative window. When a setpoint becomes infeasible, a controller slows down when moving away from a specified setpoint. When the infeasibility clears and the controller starts moving back towards the setpoint, the controller follows a tuned speed. When the controller moves in a direction against economic profit, the controller slows down and when moving in the direction of economic profit, the controller follows the tuned dynamic speed. The controller computes the best performance value for each controlled variable and is equal to the setpoint for the controlled variables with a setpoint or the highest profit value between limits for controlled values affected by economic functions.

Abstract: It is advantageous to handle certain conditions when equipment (or equipment components) is taken out-of-service or shutdown for service or maintenance. Equipment service flags may be used to indicate that an individual variable will no longer be propagated as the associated equipment has been taken out of line for service. When multiple variables feed another system or component downstream, those variables may be placed in an equipment service set and that set may be associated with an indicator that indicates that the group of variables in the equipment service set is unavailable. A visual indicator as to which variables have been made unavailable for propagation may be displayed to a user. Once the equipment or system is brought back online or is taken out of equipment service mode, the appropriate flags and equipment service set are reset and associated variables are once again propagated downstream.

Abstract: The temporary relaxation of constraints permits the efficient and cost-effective operation of certain process models. Ramp imbalances are controlled using soft-landing constraints. Such soft-landing constraints permit a smooth return to a ramp limit which permits continued operation of a system, such as a petrochemical system that includes several inflows and outflows, as opposed to a forced shut-down of the system. A ramp imbalance may be controlled by determining imbalance ramp rates and imbalance set-point ramp rates and using those constraints to resolve the dynamic control problem of a process model.

Abstract: It is advantageous to handle certain conditions when equipment (or equipment components) is taken out-of-service or shutdown for service or maintenance. Equipment service flags may be used to indicate that an individual variable will no longer be propagated as the associated equipment has been taken out of line for service. When multiple variables feed another system or component downstream, those variables may be placed in an equipment service set and that set may be associated with an indicator that indicates that the group of variables in the equipment service set is unavailable. A visual indicator as to which variables have been made unavailable for propagation may be displayed to a user. Once the equipment or system is brought back online or is taken out of equipment service mode, the appropriate flags and equipment service set are reset and associated variables are once again propagated downstream.

Abstract: The temporary relaxation of constraints permits the efficient and cost-effective operation of certain process models. Ramp imbalances are controlled using soft-landing constraints. Such soft-landing constraints permit a smooth return to a ramp limit which permits continued operation of a system, such as a petrochemical system that includes several inflows and outflows, as opposed to a forced shut-down of the system. A ramp imbalance may be controlled by determining imbalance ramp rates and imbalance set-point ramp rates and using those constraints to resolve the dynamic control problem of a process model.

Abstract: Model predictive control is used to obtain the best performance value for an objective in a dynamic environment. One or more best performance values for a model predictive application are obtained by utilizing asymmetric dynamic behavior that pushes the process to the edges of the operative window. When a setpoint becomes infeasible, a controller slows down when moving away from a specified setpoint. When the infeasibility clears and the controller starts moving back towards the setpoint, the controller follows a tuned speed. When the controller moves in a direction against economic profit, the controller slows down and when moving in the direction of economic profit, the controller follows the tuned dynamic speed. The controller computes the best performance value for each controlled variable and is equal to the setpoint for the controlled variables with a setpoint or the highest profit value between limits for controlled values affected by economic functions.