Steady-state identification with gross errors for industrial process units

Identification of steady state is an important task for satisfactory control of many processes. Due to the disadvantages of the traditional steady-state identification (SSI) methods, the adaptive polynomial filtering (APF) method was used for SSI in this paper. Furthermore, the presence of gross errors can corrupt the steady-state identification method, giving undesirable results. The APF steady-state identification with the new 3δ formula method was modified for gross errors detection by using the quartile method based on first order differential in this paper. This method was applied to the simulated data and data from a crude oil distillation unit. Simulation results and comparisons with the traditional methods confirmed the validity of the proposed method.