Structural design for on-line process optimization: I. Dynamic economics of MPC

The structural design of integrated online process optimization and regulat ory control systems based on an economic analysis of different structures i s addressed. The regulatory control laver is assumed to be implemented usin g model predictive control (MPC) techniques. Aa approach to the analysis of the dynamic economics of MPC is presented which uses the state-space formu lation as the plant model. Output feedback is performed in the framework of linear quadratic filtering theory using a Kalman filter. Using the unconst rained model predictive control law, the variance of the constrained valuab les of the closed-loop system subject to stochastic disturbances is analyze d Based on the variance of the constrained variables the amount of necessar y backoff from the constraints due to regulatory disturbances is calculated and the dynamic economics ar-e established The dynamic economics of the mo del predictive regulatory control system are incorporated into the method o f the average deviation from optimum analyzing the economic performance of an online optimization system. Thus, different structures of the integrated system of online optimization and MPC-based regulatory control can be anal yzed in terms of their economic performance, and the necessary structural d esign decisions can be taken.