|Bayesian Hybrid State Estimation for Unequal-length Batch Processes with Incomplete Observations
Guoli Ji*, Yaozong Wang, Shunyi Zhao, Yunlong Liu, Kangkang Zhang, Bin Yao, and Sun Zhou*
International Journal of Control, Automation, and Systems, vol. 15, no. 6, pp.2480-2491, 2017
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."
"Batch process, Bayesian state estimation, incomplete observations, particle filter, unequal-length batches."
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