Ethylene polymerization process has strong nonlinearity and multiple metastable states

driven by interaction between mass and energy transfer as well as compounded effect of polymerization and transport. Traditional sequential method of process design and control optimization in polymerization process is not capable of providing sufficient control freedom

which high quality products are difficult to manufacture by relying solely on design controller because of disturbance and uncertainty of process parameters. A new approach was proposed to integrate steady state design and control optimization for stable production of high performance polyethylene. The surrogate model (Kriging model) was introduced to simultaneously predict model dynamics and uncertainty. Model uncertainty was feasible space region of uncertain parameters bounded by coefficient confidence. A design performance index was defined to quantitatively interpret impact of steady state design on closed-loop dynamic behavior at process design stage. Closed-loop operating variability was quantified by model predictive controller that was to ensure process operate close to constraints and cost function of MPC that was to penalize deviations of predicted control outputs from reference operating point. The proposed method has been illustrated with integrated optimization of process design and operation control in gas-phase ethylene polymerization and method effectiveness is verified by process simulation under parameter uncertainty and disturbance.