The past few years have led to the development of a novel large-scale nonlinear programming solver called IPOPT. Described in Wachter and Biegler (2004), this algorithm uses a barrier formulation for inequality constraints and incorporates a new filter line search algorithm. It also includes a number of features that make it efficient and robust even for highly nonlinear problems. In particular, it is well suited to exploit second derivatives. The code has been used on thousands on test problems ranging in size up to almost two million variables. Finally, enhancements of this method have been made to deal with complementarity conditions that model a class of discrete decisions with continuous variables. This paper describes the development of CAPE-OPEN compliant objects for IPOPT to solve dynamic optimization problems. This activity complements a number of tasks to develop interfaces to optimization modelling platforms such as AIMMS and AMPL. Here we consider protocols such as Equation Set Objects (ESO) and the MINLP CAPE-OPEN interface to IPOPT (CO-LaN Consortium, 2002). The resulting object can be linked to other objects that are CAPE-OPEN compliant. We also describe a preprocessing procedure for generic models that leads to efficient optimization problem formulations for IPOPT. To validate the interface and the preprocessing procedure, we present a comprehensive optimization case study that links to dynamic optimization models written in gPROMS. In addition, future plans for the development, enhancement and distribution of IPOPT will be outlined.