Bayesian hybrid state estimation for unequal-length batch processes with incomplete observations

Abstract

This paper investigates state estimation problem for batch processes with unequal-length batches as well as incomplete observations. A Bayesian hybrid state estimation method is proposed based on two dimensional (2D) correlations of states. The states of equal-length segment of time are estimated according to both within-a-batch and batch-to-batch correlations, and the states of unequal-length segment are obtained according to the correlations within the batch. In this way, the batch process states can be achieved in both equal-length and unequal-length situations, of which the latter one is a more general case. In order to approximate state distribution of nonlinear system and to deal with the problem of incomplete observations, particle filter (PF) is employed. The proposed method shows its superiority with a nonlinear system and a gas-phase reaction process. Compared to a typical existing method, the proposed method provides better estimation accuracy in the situation of equal-length batches, also it shows less sensitivity to incomplete observations.