Improving steady-state identification

The use of online data together with steady-state models, as in Real Time Optimization applications, requires the identification of steady-state regimes in a process and the detection of the presence of gross erros. In this paper a method is proposed which makes use of polynomial interpolation on time windows. The method is simple because the parameters in which it is based are easy to tune as they are rather intuitive. In order to assess the performance of the method, a comparison based on Monte-Carlo simulations was performed, comparing the proposed method to three methods extracted from literature, for different noise to signal ratios and autocorrelations. The comparison was extended to real data corresponding to 197 variables of the atmospheric distillation unit of an important Brazilian refinery. A hierarchical approach was applied in order to manage the dimension of the problem.