Data-driven worst case model predictive control algorithm for propylene distillation column under uncertainty of top composition. (April 2023) - He, Renchu
Abstract: Composition soft sensors have wide application in the distillation process. In this study, considering the limitation of the prediction ability of the composition soft sensor, a data-driven worst case model predictive control of propylene distillation column is proposed to hedge against the uncertainty of top composition. Firstly, based on the compartmental method and the dynamic mechanism model, a linear state space model of the distillation column is constructed. Aiming at dealing with the uncertainty of top propylene content caused by composition soft sensor, the data-driven uncertainty set is constructed by the combination of principal component analysis and kernel density estimation based on the historical data. Then, the certainty equivalent, traditional worst case, data-driven worst case, set-point tracking and offset-free model predictive control algorithm are designed. Finally, a case study of composition control in a propylene distillation column is carried out. Compared with other strategies, the proposed algorithm ensures the quality of the product while achieving small quality surplus and low operating cost. Highlights: A novel data-driven worst case model predictive control strategy for composition control of propylene distillation column. Algorithms of certainty equivalent, traditional worst case, set-point tracking model predictive control for composition control. Construction of data-driven uncertainty set using historical data and analysis of the control performance under different parameters. A case study on composition control of the propylene distillation column through detailed comparisons of different algorithms.