Simulation Study of the Particle Filter and the EKF for State Estimation of a Large-scale DAE-system with Multi-rate Sampling1

In the present contribution we study the application of the Particle Filter (PF) and of the Extended Kalman Filter (EKF) incorporating measurements at different sampling rates for the state estimation of a large-scale model. We investigate a model of an intensified chemical process (a reactive distillation (RD) process) that is represented by a nonlinear DAE-system and has more than 100 states. The EKF and PF schemes are studied for two different cases. The performance of each of the estimation method is compared first for the case where the estimator uses a model which is identical to the process Secondly, the model used by the estimator is considered to be parametrically different from the model used to simulate the process. The effect of model-plant mismatch on the mean squared estimation error is studied for both state estimation methods. The goal is to give arguments for the selection of either of the methods to be used at the real process unit fulfilling the requirements of accurate estimation and real-time capability.