NOVEL DEVELOPMENTS IN-PROCESS OPTIMIZATION USING PREDICTIVE CONTROL

In industrial practice, constrained steady state optimisation and pred ictive control are separate, albeit closely related functions within t he control hierarchy. This paper presents a method which integrates pr edictive control with on-line optimisation with economic objectives. A receding horizon optimal control problem is formulated using linear s tate space models. This optimal control problem is very similar to the one presented in many predictive control formulations, but the main d ifference is that it includes in its formulation a general steady stat e objective depending on the magnitudes of manipulated and measured ou tput variables. This steady state objective may include the standard q uadratic regulatory objective, together with economic objectives which are often linear. Assuming that the system settles to a steady state operating point under receding horizon control, conditions are given f or the satisfaction of the necessary optimality conditions of the stea dy-state optimisation problem. The method is based on adaptive linear state space models, which are obtained by using on-line identification techniques. The use of model adaptation is justified from a theoretic al standpoint and its beneficial effects are shown in simulations. The method is tested with simulations of an industrial distillation colum n and a system of chemical reactors. (C) 1998 Elsevier Science Ltd. Al l rights reserved.