Industrial polymerization plants experience frequent changes of products, driven by end-use properties to meet various market requirements. Efficient grade transition policies are essential to save time and materials. In this study, the gas-phase catalytic polymerization is modeled in a fluidized bed reactor by a single-phase model, and dynamic optimization is implemented to determine optimal operating sequences for grade changes. Two optimization formulations, a single-stage and a multi-stage formulation, are introduced and compared. The superiority of the multi-stage formulation is demonstrated owing to a better control on each stage during the transition and a further reduction of off-grade time. Subsequently, an on-line optimal control framework is established by incorporating shrinking horizon nonlinear model predictive control with an expanding horizon weighted least-square estimator for process states and unknown parameters. The results of a case study indicate the designed framework is able to handle process uncertainty, while reducing the transition time. (C) 2020 Elsevier Ltd. All rights reserved.