Real-time feasibility of nonlinear predictive control for large scale processes-a case study

Despite many control theoretic and numerical advances, up to now there is no realistic feasibility study of modern nonlinear model predictive control (NMPC) schemes for the real-time control of large-scale processes. In this paper the application of NMPC to a nontrivial process control example, namely the control of a high-purity binary distillation column, is considered. Using models of different complexity and different control schemes, the computational load, resulting closed loop performance and the effort needed to design the controllers is compared. It is shown that a real-time application of modern NMPC schemes is feasible with existing techniques, even for a 164/sup th/ order model with a sampling time of 30 s, if a state of the art dynamic optimization algorithm and an efficient NMPC scheme are used.