A multi-scenario nonlinear model predictive control approach for robust product transitions

Abstract

Dynamic product transitions are ubiquitous operations in the processing industry. When a first-principles dynamic model is deployed for real system representation, the calculation of the dynamic optimal trajectory for product transition can be cast as an optimal control problem. A common practice in addressing the solution of optimal product transitions lies in the assumption of free of uncertainty first-principle models. Ignoring the effect of model uncertainty on product transitions can result in unfeasible dynamic trajectories. In this work, an optimization scenario approach, featuring variable scenario weighting functions, is deployed for assessing the impact of model uncertainty on the control actions such that feasible and optimal transition trajectories are computed featuring minimum deviation from target values. The optimization approach was applied to three nonlinear reaction systems. The results demonstrate that when the variable weighting optimization scenario approach is suitable for approximating model uncertainty, feasible transition trajectories can be calculated at relatively low computational cost (for small or medium scale systems).