Dynamic optimization of complex distributed process systems

6 páginas, 3 tablas, 4 figurasThe dynamicoptimization (DO) of complexdistributed parameter systems (DPSs), like e.g., reaction–diffusion processes, is a challenging task. Most of the existing numerical approaches imply a large computational effort, therefore precluding its application to demanding applications like real time DO or model predictive control. This work, based on the control vector parameterization (CVP) approach, describes two ways to enhance the efficiency of the resulting nonlinear programming (NLP) problem solution. On the one hand, the convergence properties of the NLP solver are enhanced through the use of exact gradients and projected Hessians (H.p). On the other hand, simulation efficiency is improved through the use of reduced order descriptions of the DPSs. The capabilities and possibilities of these two enhancements are illustrated with a number of complexdistributed case studiesThe authors thank the Spanish Ministry of Science and Technology (MCyT project AGL2001-2610-C02-02) and Xunta de Galicia (grant GIDIT02PXIC40211PN) for financial support.Peer reviewe