Abstract: A process is modeled by a dynamic model, handling time dependent relations between manipulated variables of different process sections (10A–D) and measured process output variables. Suggested input trajectories for manipulated variables for a subsequent time period are obtained by optimizing an objective function over a prediction time period, under constraints imposed by the dynamic process model and/or preferably a production plan for the same period. The objective function comprises relations involving predictions of controlled process output variables as a function of time using the process model, based on the present measurements, preferably by a state estimation procedure. By the use of a prediction horizon, also planned future operational changes can be prepared for, reducing any induced fluctuations. In pulp and paper processes, process output variables associated with chemical additives can be used, adapting the optimization to handle chemical additives aspects.

Abstract: A process is modeled by a dynamic model, handling time dependent relations between manipulated variables of different process sections (10A-D) and measured process output variables. Suggested input trajectories for manipulated variables for a subsequent time period are obtained by optimizing an objective function over a prediction time period, under constraints imposed by the dynamic process model and/or preferably a production plan for the same period. The objective function comprises relations involving predictions of controlled process output variables as a function of time using the process model, based on the present measurements, preferably by a state estimation procedure. By the use of a prediction horizon, also planned future operational changes can be prepared for, reducing any induced fluctuations. In pulp and paper processes, process output variables associated with chemical additives can be used, adapting the optimization to handle chemical additives aspects.