Reduced-Order Models for Nonlinear Distributed Process Systems and Their Application in Dynamic Optimization

Dynamic optimization of distributed process systems has received considerable attention over the last couple of years. Most approaches proposed for the solution of these types of problems are based on the use of the control vector parametrization method, which transforms the original dynamic optimization problem into an outer nonlinear programming (NLP) problem. The solution of this NLP problem requires the simulation of the process under consideration for each function evaluation. Unfortunately, this task is usually very demanding for this class of dynamic systems, which calls for reduced-order descriptions of the distributed process system. In this work, we exploit the use of low-order models based on the Galerkin projection on a set of proper orthogonal functions as a very efficient alternative to the solution of dynamic optimization problems for nonlinear distributed process systems.