Economic Model Predictive Control of Nonlinear Process Systems Using Empirical Models

Economic model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first principles or through system identification techniques. In industrial practice, it may be difficult in general to obtain an accurate first-principles model of the process. Motivated by this, in the present work, Lyapunov-based EMPC (LEMPC) is designed with a linear empirical model that allows for closed-loop stability guarantees in the context of nonlinear chemical processes. Specifically, when the linear model provides a sufficient degree of accuracy in the region where time varying economically optimal operation is considered, conditions for closed-loop stability under the LEMPC scheme based on the empirical model are derived. The LEMPC scheme is applied to a chemical process example to demonstrate its closed-loop stability and performance properties as well as significant computational advantages. (c) 2014 American Institute of Chemical Engineers AIChE J, 61: 816-830, 2015