摘要：Real-Time Optimization (RTO) is not always able to achieve optimal process operation due to the presence of significant uncertainty about the plant models that are used to make decisions and also due to the differences between control architecture layers which operate on different time-scales and use different kind of models. To overcome these issues the economic optimization problem is modified following the Modifier Adaptation methodology to bring the process to the real optimum despite the presence of uncertainty by using plant measurements. To deal with parametric and structural plant-model mismatch, a new approach is presented that combines the estimation of process gradients from transient and steady-state information. It speeds up the convergence of Modifier Adaptation methodology to the process optimum. The approach is illustrated through the simulated example of a depropanizer distillation column.
