Simultaneous optimization and control for polypropylene grade transition with two-layer hierarchical structure

In this paper, a two-layer hierarchical structure of optimization and control for polypropylene grade transition was raised to overcome process uncertain disturbances that led to the large deviation between the open-loop reference trajectory and the actual process. In the upper layer, the variant time scale based control vector parametric methods (VTS-CVP) was used for dynamic optimization of transition reference trajectory, while nonlinear model predictive controller (NMPC) based on closed-loop subspace and piece-wise linear (SSARX-PWL) model in the lower layer was tracking to the reference trajectory from the upper layer for overcoming high-frequency disturbances. Besides, mechanism about trajectory deviation detection and optimal trajectory updating online were introduced to ensure a smooth transition for the entire process. The proposed method was validated with the real data from an industrial double-loop propylene polymerization reaction process with developed dynamic mechanism mathematical model.

Graphical abstractIn order to overcome uncertain disturbance during the grade transition process of polypropylene production, this work combine the dynamic optimization of transition trajectories and predictive control of trajectory tracking in the two-layer hierarchical structure for simultaneous optimization and control for double loop polypropylene grade transition. In the upper D-RTO layer, the variant time-scale based control vector parameterization method, which provides the optimal input and output reference trajectory to the lower controller, is adopted for grade transition optimization on the basis of mathematical model of double-loop polypropylene reaction process. While in the lower layer, the NMPC tracking controller based on SSARX-PWL model obtained from parameter identification is used to fast track the reference trajectory and overcome high frequency process disturbances. In addition, the sample-wise adaptive sliding window technique and deviation detection and update methods for transition trajectory are introduced by taking into account of the uncertain disturbance.Figure optionsDownload full-size imageDownload as PowerPoint slide