Piecewise Affine Modeling and Robust Model Predictive Control of a Distillation Column Considering Multiple Prediction Trajectories

A new method based on piecewise affine approximation is proposed to model a distillation column and a novel robust MPC is addressed for PWA systems considering multiple prediction trajectories. Distillation columns have highly nonlinear and complex behavior. However, even in a rigorous dynamical model a number of model simplifications are included. Piecewise affine maps have universal approximation properties which are useful for modeling of nonlinear systems in a wide range of operation. Model predictive control for PWA systems faces multiple prediction trajectories at each sample time due to different system dynamics over prediction horizon. Thus, the computational burden increases exponentially with the prediction horizon length. In order to decrease the computational burden, a suboptimal method is used to solve MIQP problems in MPC for PWA systems in presents of model uncertainty and disturbances. A real distillation column of a debutanizer unit in South Pars Gas refineries is modeled with PWA method and validated using the appropriate nonlinear model. A tube-based model predictive control proposed in such a way that the optimization problem can be solved considering multiple prediction trajectories at each sample time. In the proposed method, the computational burden is increased linearly by the prediction horizon length.