Interdisciplinary Center for Scientific Computing
University of Heidelberg
A major challenge for any nontrivial NMPC application is the real-time optimization of large scale process models of differential algebraic (DAE) or partial differential equation (PDE) type. We present an efficient embedding technique to initialize subsequent problems, implemented in an online algorithm for NMPC, that has already been applied to experimentally control a pilot plant distillation column described by DAE. We show how the technique can be exteded to very large scale DAE systems, arising from the discretization of instationary PDEs by the method of lines, and apply this technique to a periodically operated chromatographic separation process.