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

•Modifier Adaptation methodology uses plant measurements to bring the process to the real optimum despite the presence of uncertainty.•A Modifier Adaptation approach is proposed to speed up the convergence to the optimum using transient measurements to estimate the process gradients.•The described method is valid for parametric and structural uncertainty.•The approach is illustrated through a simulated depropanizer distillation column.

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.