Industrial polymerization plants experience frequent changes of products, driven by end-use properties to meet various market requirements. The transition between different product grades usually takes hours, when tons of off-specification polymers are generated. Therefore, good operation policies are essential to save time and materials. In this study, the gas-phase catalytic polymerization is modeled in a fluidized bed reactor (FBR) and dynamic optimization is implemented to determine optimal operating sequences for grade changes. To capture dynamics of the FBR, a single-phase model is applied by assuming well mixed catalyst particles and monomer gases. Unreacted gases are recycled and cooled down to a partially condensed mode to remove heat generated by reactions and increase production rate. The dynamic optimal grade transition is performed on this FBR model by two optimization formulations, a single-stage and a multi-stage formulation. 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. In the case study, a more than 60% shorter transition time is obtained by the multi-stage optimization compared with a PID controlled transition. 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 designed framework is able to overcome certain levels of uncertainty, while reducing the transition time.
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