This study focuses on the performance of large-scale nonlinear programming (NLP) solvers for the dynamic optimization in real-time of large processes. The MATLAB-based OptControlCentre (OCC) is coupled with large-scale optimization tools and developed for on-line, real-time dynamic optimization. To demonstrate these new developments, we consider the on-line, real-time dynamic optimization of the Tennessee Eastman (TE) challenge process in a nonlinear model predictive control (NMPC) framework. The example captures the behavior of a typical industrial process and consists of a two phase reactor, where an exothermic reaction occurs, along with a flash, a stripper, a compressor and a mixer. The process is nonlinear and open loop unstable; without control it reaches shutdown limits within an hour, even for very small disturbances. The system is represented through a first principles model with about 200 differential algebraic equations (DAEs). As a result, the NMPC formulation of this system presents some interesting features for dynamic optimization approaches. This study compares two state-of-the-art NLP solvers, SNOPT and IPOPT, for dynamic optimization on a number of challenging control scenarios, and illustrates some of the advantages of IPOPT for dynamic optimization. (C) 2003 Elsevier Science Ltd. All rights reserved.