Dynamic optimization problems are usually solved by transforming them into nonlinear programming (NLP) models with either sequential or simultaneous approaches. In this paper, the potential and limitations of both procedures in solving a general batch nylon-6,6 autoclave optimization model are evaluated and discussed. Nylon-6,6 is a highly value-added good produced in large-scale under many different specifications. Highly nonlinear behavior, lack of on-line measurements of polymer qualities and several disturbances inherent to this process motivate its optimal operation. In addition, the operation during the finishing stage of the batch polycondensation is crucial since it largely determines the final product molecular weight and quality. Results show that both strategies can be successfully applied to solve the dynamic optimization problem only after an expressive level of investments in terms of modeling implementation. However, the simultaneous strategy has the advantage that specification constraints can be directly enforced in the model, thus generating better and more robust results. (C) 2003 Elsevier B.V. All rights reserved.