Large-scale nonlinear programming using IPOPT: An integrating framework for enterprise-wide dynamic optimization

Integrationof real-timeoptimization and controlwithhigher level decision-making (scheduling andplanning) is an essential goal for profitable operation in a highly competitive environment. While integrated large-scale optimization models have been formulated for this task, their size and complexity remains a challenge to many available optimization solvers. On the other hand, recent development of powerful, large-scale solvers leads to a reconsideration of these formulations, in particular, through development of efficient large-scale barrier methods for nonlinear programming (NLP). As a result, it is now realistic to eal-time optimization odel predictive control oving horizon estimation onlinear programming ensitivity solve NLPs on the order of amillion variables, for instance, with the IPOPT algorithm.Moreover, the recent NLP sensitivity extension to IPOPT quickly computes approximate solutions of perturbedNLPs. This allows on-line computations to be drastically reduced, even when large nonlinear optimization models are considered. These developments are demonstrated on dynamic real-time optimization strategies that can be used tomerge and replace the tasks of (steady-state) real-time optimization and (linear)model predictive control. We consider a recent case study of a low density polyethylene (LDPE) process to illustrate these concepts.