Online composition estimation and experiment validation of distillation processes with switching dynamics

This study explores both the theoretical and experimental investigations of applying a continuum and noncontinuum state estimator to composition estimation in a distillation process with switching dynamics. In a hybrid distillation modeling, the column compositions are considered as continuum states while the operating modes are modeled as noncontinuum states. A moving horizon estimator (MHE), which has the capability to handle process constraints is developed for composition estimation in a distillation process under known switching mode criteria using the available temperature measurements. The performance of a MHE is shown to be better than that of EKF in handling process and measurement noise under switching dynamics. For some situations where the system operating mode transition is unknown, a new approach to state estimation under unknown switching functions is investigated. The proposed method combines a MHE for composition estimation with a mode change detector to detect a change in the system operating mode and an operating mode estimator to identify the functioning mode. In the presence of both the measurement noise and plant-model mismatch, the developed estimator is shown to be effective in estimating both the column composition and the system operating mode accurately.