Industrial polymerization plants experience frequent changes of products, driven by end-use properties to meet various market requirements. Good 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 concluded owing to better control at each stage during the transition and a further reduction of off-grade time. Subsequently, an on-line optimal control framework is established by incorporating a shrinking horizon nonlinear model predictive control with an expanding horizon weighted least-squares estimator for process states and unknown parameters. The results of a case study indicate the designed framework is able to overcome certain levels of uncertainty, while reducing the transition time.
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