Modifier Adaptation methodology based on transient and static measurements for RTO to cope with structural uncertainty

Abstract

Optimal process operation is carried out by a Real-Time Optimization (RTO) layer which is not always able to achieve its targets due to the presence of plant-model mismatch. To overcome this issue, the economic optimization problem solved in the RTO is changed following the Modifier Adaptation methodology (MA), which uses plant measurements to find a point that satisfies the necessary optimality conditions (NCO) of an uncertain process. MA proceeds by iteratively adjusting the optimization problem with first and zeroth order corrections, calculated from steady-state information at each RTO execution. This implies a long convergence time. This paper presents a new method based on a recursive identification algorithm to estimate process gradients from transient measurements to speed up the convergence of MA. The proposed approach is implemented in a simulated depropanizer column that incorporates a simplified model in the RTO, reducing by 8 the convergence time compared with traditional MA.