Efficient formulation and solution of nonlinear model predictive control problem

Large Scale optimization strategies for process operation have evolved sign ificantly over the past two decades. Currently, continuous variable optimiz ation problems (nonlinear programs) can be solved on-line Fur steady state refinery models with several hundred thousand variables. Moreover, efficien t NLP strategies have been developed for dynamic optimization problems. Sti ll, the next step, on-line optimization of large dynamic chemical processes , requires the tackling of a number of limitations and research challenges. Many of the advances in NLP algorithms have taken place by recognizing and exploiting the framework of Successive Quadratic Programming (SQP) algorit hms. These are extensions of Newton type methods for converging to the solu tion of the KKT (optimality) conditions of the optimization problem. Moreov er, a number of innovations in algorithm design and problem formulation can greatly improve performance. ks a result, very fast NLP algorithms can be derived for data reconciliation, parameter estimation, nonlinear model pred ictive control and dynamic optimization. Moreover, inequality constraints a nd variable bounds can be treated through advances in interior point strate gies. These methods preserve the particular problem structure and scale wel l in performance for large-scale problems with many constraints. In particu lar, we will consider the application of these strategics to problems in No nlinear Model Predictive Control (NMPC). In parallel to the development of efficient nonlinear programming algorithms, we also need to consider the in corporation of nonlinear models that are accurate but also easily identifie d and implemented. Here we consider DABNet (decoupled A-B neural network) m odels which have very desirable approximation properties for a wide variety of nonlinear systems. These models can be incorporated easily into the NMP C framework and lead to very efficient online control algorithms. Finally, the concepts developed in this paper will be illustrated by several case st udies drawn from our previous work. These examples illustrate the importanc e of nonlinear model predictive control and emphasize the need for efficien t online computation.